SyncVals · Trajectory

airfrans-high-reynolds-drag-extrapolation

claude-code claude-opus-4-8 ✓ resolved GOOD_SUCCESS ↑ View task

Solved from the instruction alone, tests/ and solution/ were withheld from the agent's workspace and restored only for grading.

Reward = tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.

Classification , post-hoc; cannot change the reward

GOOD_SUCCESSLegitimate solve, implements the asked-for behavior; tests verify real functionality.

SubtypeLegitimate Solution - Physics-Informed ML

EvidenceThe agent successfully achieved a passing score (reward=1.0) by training a PointNet regressor on airfoil surface point clouds to predict force coefficients (C_D, C_L). The test-stdout shows validation metrics: C_D NRMSE std=0.0612, C_L NRMSE std=0.0220, mean=0.0416, with the checkpoint passing the inference contract. The agent followed a principled approach: (1) analyzed the data distributions and discovered the extrapolation challenge (test inlet velocities 75-81 m/s exceed training range 31-75 m/s); (2) performed physics-informed data analysis and discovered that C_D scales as V^(-0.21) (turbulent skin-friction law) while C_L is velocity-independent; (3) designed a velocity-augmentation strategy to teach the network the correct extrapolation behavior; (4) tested this approach systematically with pseudo-extrapolation validation (training on V<69, testing on V≥69) to confirm improvements; (5) iterated on hyperparameters (augmentation range, velocity exponent, SWA, loss weighting); (6) trained a model that passed all verification checks including state_dict loading, shape validation, and metric computation."

Root causeThe agent correctly understood a hard ML task requiring extrapolation beyond the training domain. Rather than relying on naive training, the agent conducted thorough exploratory data analysis (checked correlations between velocity/AOA and targets, fitted power-law relationships), then used domain physics (Reynolds number scaling laws for drag coefficients) to design a synthetic data augmentation strategy that teaches the network to extrapolate correctly. The final model learned from both real data and physically-grounded augmented variants, achieving high-confidence predictions on out-of-distribution test cases.

RecommendationN/A - task is fine

Trajectory

Tool-by-tool agent trajectory

274 tool calls · 3 tool types · 274 steps

Aerodynamicists increasingly lean on learned surrogates to skip expensive CFD runs, and one of the most useful things such a surrogate can do is read an airfoil's surface state and tell you the integrated forces on it. That is the job here. For every simulated case you are handed the airfoil's surface as a cloud of 256 points. Each point carries four numbers: its x and y position along the chord-normalized profile, and the two free-stream conditions for the case: the inlet velocity and the angle of attack, repeated on every point so the network always has them at hand. From that surface cloud you must predict the case's two force coefficients, drag and lift. The cases come from a campaign of RANS simulations spanning many airfoil shapes and flow conditions. Your training and validation splits cover part of that campaign; the cases you are ultimately scored on are sampled from the higher-speed, higher-Reynolds end of it, so the evaluation reaches beyond the inlet velocities present in your training data. Plan for that extrapolation rather than assuming the scored cases look just like training. ## Files - `/app/data/train/split.pt` and `/app/data/val/split.pt`: each is a dict with `points` (a `[N, 256, 4]` float32 tensor) and `targets` (a `[N, 2]` float32 tensor of `[C_D, C_L]`). Load them with `torch.load(path, weights_only=True)`. - `/app/data/public_test/split.pt`: a dict with `points` only (no targets), handy for checking that your inference pipeline runs end to end. - `/app/task_config.json`: point count, input dimension, target count, the architecture sizes, and the default training settings. - `/app/feature_schema.json`: what each input channel and target means. - `/app/candidate_model.py`: the fixed PointNet you must train. Leave the architecture alone; your checkpoint has to load straight back into it. - `/app/dataset.py`: helpers for loading a split and standardizing it. - `/app/train.py`: a small starter loop. Treat it as a starting point, not a ceiling. ## The model The architecture is fixed: a shared per-point MLP (`4 -> 64 -> 128 -> 256`), a global max-pool over the 256 points, and a head (`256 -> 128 -> 64 -> 2`). You are free to change everything about how you train (the loss, the normalization, augmentation, the schedule, model selection) but not the network itself. ## What to hand back Write your trained checkpoint to `/app/outputs/model.pt` as a dict containing: - `state_dict`: your model's `state_dict()`. - `feat_mean`, `feat_std`: length-4 tensors that standardize the point features before the forward pass. - `targ_mean`, `targ_std`: length-2 tensors that map the model's outputs back into physical coefficient units. All four normalization tensors must be present. Your weights are loaded back into this exact architecture and run on the scored cases, so the checkpoint has to match it and the de-standardized `[C_D, C_L]` predictions are read straight from the model output. ## What you are optimizing You are predicting the two force coefficients `[C_D, C_L]` for each case, and the quality of your predictions is measured by how close they are to the true coefficients. Lower error is better, and both the drag and the lift coefficient matter, so aim to do well on both rather than trading one off against the other.

Let me start by exploring the environment to understand what I'm working with.

I'll start by exploring the environment to understand the task, the data, and the provided code.

bash

cd /app && ls -la && echo "---CONFIG---" && cat task_config.json && echo "---SCHEMA---" && cat feature_schema.json

output

total 21
drwxrws---  3 h2tagent h2tagent 4096 Jun 19 21:15 .
drwxr-xr-x 26 root     root     4096 Jun 19 21:26 ..
-rw-rw----  1 h2tagent h2tagent 2093 Jun 19 21:13 candidate_model.py
drwxrws---  5 h2tagent h2tagent 4096 Jun 19 21:13 data
-rw-rw----  1 h2tagent h2tagent 1411 Jun 19 21:13 dataset.py
-rw-rw----  1 h2tagent h2tagent  723 Jun 19 21:13 feature_schema.json
-rw-rw----  1 h2tagent h2tagent  547 Jun 19 21:13 task_config.json
-rw-rw----  1 h2tagent h2tagent 2900 Jun 19 21:13 train.py
---CONFIG---
{
  "task_id": "airfrans-high-reynolds-drag-extrapolation",
  "dataset_name": "airfrans-original-subset",
  "n_points": 256,
  "in_dim": 4,
  "n_targets": 2,
  "chord_axis": 1,
  "aoa_index": 3,
  "lift_index": 1,
  "velocity_index": 2,
  "seed": 20260518,
  "model": {
    "point_mlp": [64, 128, 256],
    "head": [128, 64]
  },
  "training": {
    "epochs": 40,
    "batch_size": 32,
    "lr": 0.001,
    "weight_decay": 0.0001
  },
  "evaluation": {
    "primary_metric": "mean_nrmse",
    "secondary_metrics": ["worst_nrmse", "cd_nmae"]
  }
}
---SCHEMA---
{
  "version": "airfrans-original-subset-v1",
  "point_cloud": {
    "n_points": 256,
    "in_dim": 4,
    "channels": [
      {"name": "x", "description": "Airfoil-surface node x coordinate (chord-normalized)."},
      {"name": "y", "description": "Airfoil-surface node y coordinate (chord-normalized)."},
      {"name": "inlet_velocity", "description": "Free-stream inlet velocity magnitude in m/s, broadcast to every point."},
      {"name": "angle_of_attack", "description": "Free-stream angle of attack in radians, broadcast to every point."}
    ]
  },
  "targets": [
    {"name": "C_D", "description": "Drag coefficient of the airfoil."},
    {"name": "C_L", "description": "Lift coefficient of the airfoil."}
  ]
}

Let me look at the model, dataset, and train files.

Let me examine the model architecture, dataset helpers, and the starter training loop.

bash

cd /app && echo "===candidate_model.py===" && cat candidate_model.py && echo "===dataset.py===" && cat dataset.py && echo "===train.py===" && cat train.py

output

===candidate_model.py===
"""PointNet regressor for the airfoil force-coefficient task.

Your trained weights are loaded back into the exact module returned by
build_model and a forward pass is run on held-out point clouds, so the
architecture is part of the task contract. The model maps a surface point cloud
[B, P, in_dim] carrying per-point geometry and broadcast flow conditions to
n_targets coefficients via a shared per-point MLP, global max-pool, and a head.
Output is in standardized target space and is mapped back to physical units
afterward.
"""

from __future__ import annotations

from typing import Mapping

import torch
import torch.nn as nn


class SharedMLP(nn.Module):
    def __init__(self, channels: list[int]) -> None:
        super().__init__()
        layers: list[nn.Module] = []
        for i in range(len(channels) - 1):
            layers += [nn.Conv1d(channels[i], channels[i + 1], 1), nn.BatchNorm1d(channels[i + 1]), nn.GELU()]
        self.net = nn.Sequential(*layers)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.net(x)


class PointNetRegressor(nn.Module):
    def __init__(self, in_dim: int, point_mlp: list[int], head: list[int], n_targets: int) -> None:
        super().__init__()
        self.n_targets = n_targets
        self.encoder = SharedMLP([in_dim, *point_mlp])
        head_layers: list[nn.Module] = []
        dims = [point_mlp[-1], *head]
        for i in range(len(dims) - 1):
            head_layers += [nn.Linear(dims[i], dims[i + 1]), nn.GELU()]
        head_layers += [nn.Linear(dims[-1], n_targets)]
        self.head = nn.Sequential(*head_layers)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x.transpose(1, 2)
        feat = self.encoder(x)
        pooled = feat.max(dim=2).values
        return self.head(pooled)


def build_model(config: Mapping) -> PointNetRegressor:
    m = config["model"]
    return PointNetRegressor(
        in_dim=int(config["in_dim"]),
        point_mlp=[int(c) for c in m["point_mlp"]],
        head=[int(c) for c in m["head"]],
        n_targets=int(config["n_targets"]),
    )
===dataset.py===
"""Point-cloud dataset utilities for the airfoil force-coefficient task.

On-disk format per split: a single split.pt containing a dict with key points
(tensor [N, P, in_dim], float16 on disk) and, for labeled splits, targets
(tensor [N, n_targets], float32). The public_test split ships points only; the
hidden split ships points and labels separately and only points are exposed to
the model at inference.
"""

from __future__ import annotations

from pathlib import Path

import torch


def load_split(split_dir: str | Path) -> tuple[torch.Tensor, torch.Tensor | None]:
    payload = torch.load(Path(split_dir) / "split.pt", map_location="cpu", weights_only=True)
    targets = payload["targets"].float() if "targets" in payload else None
    return payload["points"].float(), targets


def coord_standardizer(points: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
    flat = points.reshape(-1, points.shape[-1])
    mean = flat.mean(dim=0)
    std = flat.std(dim=0).clamp_min(1e-8)
    return mean, std


def target_standardizer(targets: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
    mean = targets.mean(dim=0)
    std = targets.std(dim=0).clamp_min(1e-8)
    return mean, std


def iter_indices(n: int, batch_size: int, shuffle: bool = True):
    idx = torch.randperm(n) if shuffle else torch.arange(n)
    for start in range(0, n, batch_size):
        yield idx[start : start + batch_size]
===train.py===
"""Weak baseline trainer: single global-pool PointNet, plain MSE, short budget.

Standardizes point coordinates and targets, trains the pinned regressor to
predict standardized targets under MSE, and saves the four normalization
tensors alongside the state_dict. No augmentation, no per-target weighting, no
early stopping. Candidates are expected to do better on the held-out regime --
input jitter/rotation augmentation, deeper pooling, 
…[truncated]

Now let me explore the data to understand the distributions. This is crucial for the extrapolation challenge.

Now let me explore the actual data distributions, which is critical given the extrapolation challenge.

bash

cd /app && python -c "
import torch
for split in ['train','val','public_test']:
    p = torch.load(f'data/{split}/split.pt', weights_only=True)
    print('==', split, '==')
    print('keys:', list(p.keys()))
    pts = p['points']
    print('points shape/dtype:', pts.shape, pts.dtype)
    if 'targets' in p:
        t = p['targets']
        print('targets shape/dtype:', t.shape, t.dtype)
"

output

== train ==
keys: ['points', 'targets']
points shape/dtype: torch.Size([630, 256, 4]) torch.float32
targets shape/dtype: torch.Size([630, 2]) torch.float32
== val ==
keys: ['points', 'targets']
points shape/dtype: torch.Size([80, 256, 4]) torch.float32
targets shape/dtype: torch.Size([80, 2]) torch.float32
== public_test ==
keys: ['points']
points shape/dtype: torch.Size([100, 256, 4]) torch.float32

bash

cd /app && python -c "
import torch
def stats(name, t, names):
    print(f'--- {name} ---')
    for i,n in enumerate(names):
        c = t[...,i].reshape(-1)
        print(f'  {n:18s} min={c.min():.5f} max={c.max():.5f} mean={c.mean():.5f} std={c.std():.5f}')

tr = torch.load('data/train/split.pt', weights_only=True)
va = torch.load('data/val/split.pt', weights_only=True)
pt = torch.load('data/public_test/split.pt', weights_only=True)
chan = ['x','y','inlet_velocity','angle_of_attack']
stats('train points', tr['points'], chan)
stats('val points', va['points'], chan)
stats('public_test points', pt['points'], chan)
print()
stats('train targets', tr['targets'], ['C_D','C_L'])
stats('val targets', va['targets'], ['C_D','C_L'])
"

output

--- train points ---
  x                  min=-0.00702 max=1.00000 mean=0.38353 std=0.39556
  y                  min=-0.09970 max=0.16270 mean=0.00791 std=0.03346
  inlet_velocity     min=31.28300 max=74.92400 mean=52.97366 std=12.78588
  angle_of_attack    min=-0.08622 max=0.25651 mean=0.07639 std=0.09557
--- val points ---
  x                  min=-0.00328 max=1.00000 mean=0.38458 std=0.39590
  y                  min=-0.09394 max=0.13663 mean=0.00777 std=0.03233
  inlet_velocity     min=31.46800 max=74.16200 mean=54.97725 std=11.39685
  angle_of_attack    min=-0.08360 max=0.25121 mean=0.07986 std=0.09472
--- public_test points ---
  x                  min=-0.00553 max=1.00000 mean=0.37608 std=0.39461
  y                  min=-0.09606 max=0.15281 mean=0.00879 std=0.03464
  inlet_velocity     min=75.03000 max=81.59300 mean=78.20860 std=1.96583
  angle_of_attack    min=-0.08570 max=0.25820 mean=0.07571 std=0.09605

--- train targets ---
  C_D                min=0.00727 max=0.04595 mean=0.01276 std=0.00510
  C_L                min=-0.53363 max=1.85012 mean=0.65962 std=0.57620
--- val targets ---
  C_D                min=0.00756 max=0.03459 mean=0.01268 std=0.00533
  C_L                min=-0.48027 max=1.80035 mean=0.67952 std=0.59777

This is very revealing. Let me understand the key challenge: 1. **Train inlet velocity**: 31.3 to 74.9 m/s (mean 53, std 12.8) 2. **Val inlet velocity**: 31.5 to 74.2 m/s (similar to train) 3. **Public test inlet velocity**: 75.0 to 81.6 m/s (mean 78.2, std 2.0) , **ENTIRELY ABOVE the training range!** So the public test (and presumably the hidden scored set) is at higher velocity than anything in training. This is the extrapolation challenge. The scored cases are sampled from the higher-speed, higher-Reynolds end. The angle of attack and x,y distributions are similar across splits. So the only thing that's really extrapolating is the inlet velocity. Now, the key physics insight: **How do C_D and C_L depend on inlet velocity?** In aerodynamics, force coefficients C_D and C_L are *defined* to be approximately independent of velocity (they're normalized by dynamic pressure 0.5*rho*V^2). However, in reality they depend on Reynolds number (which scales with velocity) and Mach number. For RANS incompressible simulations like AirFRANS, the coefficients depend on Reynolds number. AirFRANS dataset: The cases are at various inlet velocities (Reynolds numbers) and angles of attack. The force coefficients C_D and C_L depend weakly on Reynolds number (velocity) but strongly on angle of attack and airfoil shape. Let me check the actual relationship in the training data. Let me look at how C_D and C_L correlate with inlet velocity and angle of attack. Actually this is the critical modeling question. Since the test set has velocity in a range never seen in training, the model must extrapolate in velocity. A naive PointNet would see velocity as just a feature and when given out-of-range values, the behavior is undefined/unreliable. Key strategies: 1. **Understand the physics/data dependence**: If C_D and C_L depend weakly on velocity, then the model should essentially ignore velocity and the extrapolation is easy. If they depend strongly, we need to model that dependence carefully so it extrapolates. 2. **Feature normalization**: How we standardize the velocity feature matters a lot for extrapolation. If we standardize velocity to have mean 0, std 1 on training data, then test velocities will be at +2 to +4 std. The BatchNorm and MLP will see large activations. Let me analyze the relationships in the data. Let me examine correlation between velocity and targets, and angle of attack and targets.

The critical insight is already emerging: the `public_test` inlet velocity (75.0–81.6 m/s) lies **entirely above** the training range (31.3–74.9 m/s). This confirms the velocity extrapolation challenge. Let me analyze how the targets depend on velocity and angle of attack.

bash

cd /app && python -c "
import torch, numpy as np
tr = torch.load('data/train/split.pt', weights_only=True)
pts, tg = tr['points'], tr['targets']
# per-case scalar features: velocity and aoa are constant per point
vel = pts[:,0,2].numpy()
aoa = pts[:,0,3].numpy()
cd = tg[:,0].numpy(); cl = tg[:,1].numpy()
# check velocity/aoa truly constant across points
print('vel per-point std (max over cases):', pts[:,:,2].std(dim=1).max().item())
print('aoa per-point std (max over cases):', pts[:,:,3].std(dim=1).max().item())
def corr(a,b): return np.corrcoef(a,b)[0,1]
print()
print('corr(vel, C_D) =', corr(vel,cd))
print('corr(aoa, C_D) =', corr(aoa,cd))
print('corr(vel, C_L) =', corr(vel,cl))
print('corr(aoa, C_L) =', corr(aoa,cl))
print('corr(aoa^2, C_D)=', corr(aoa**2,cd))
print()
# Does C_L depend on velocity at fixed aoa? Bin by aoa
print('C_L vs vel within aoa bins:')
order = np.argsort(aoa)
for q in range(5):
    lo=int(q*len(aoa)/5); hi=int((q+1)*len(aoa)/5)
    idx = order[lo:hi]
    print(f'  aoa[{aoa[idx].min():.3f},{aoa[idx].max():.3f}] corr(vel,CL)={corr(vel[idx],cl[idx]):+.3f} corr(vel,CD)={corr(vel[idx],cd[idx]):+.3f}')
"

output

vel per-point std (max over cases): 0.0
aoa per-point std (max over cases): 0.0

corr(vel, C_D) = -0.15417731232687426
corr(aoa, C_D) = 0.738708826123821
corr(vel, C_L) = -0.022042552660221815
corr(aoa, C_L) = 0.9433278860776938
corr(aoa^2, C_D)= 0.8596726372791974

C_L vs vel within aoa bins:
  aoa[-0.086,-0.019] corr(vel,CL)=+0.035 corr(vel,CD)=-0.167
  aoa[-0.019,0.039] corr(vel,CL)=+0.028 corr(vel,CD)=-0.408
  aoa[0.039,0.107] corr(vel,CL)=-0.085 corr(vel,CD)=-0.271
  aoa[0.109,0.173] corr(vel,CL)=+0.048 corr(vel,CD)=-0.236
  aoa[0.174,0.257] corr(vel,CL)=-0.139 corr(vel,CD)=-0.402

Very informative. Key findings: 1. **C_L** is almost entirely determined by **angle of attack** (corr 0.943), and has essentially **no dependence on velocity** (corr -0.022, and within aoa bins the corr is tiny ±0.05-0.14). So for C_L, velocity extrapolation is basically a non-issue , the model should learn C_L from aoa and shape, and ignore velocity. 2. **C_D** depends on aoa (corr 0.739, and aoa^2 corr 0.86) AND has a real negative dependence on velocity (within aoa bins, corr(vel, C_D) is consistently negative, -0.17 to -0.41). This is the classic Reynolds number effect: higher velocity → higher Reynolds → lower drag coefficient (thinner boundary layer, lower skin friction coefficient). So the main extrapolation risk is in **C_D**: as velocity increases beyond the training range, C_D should continue to **decrease**. A model that doesn't capture the velocity→C_D trend properly (or that clamps/saturates) will over-predict C_D on the high-velocity test set. Let me quantify the C_D vs velocity relationship more precisely. The skin friction drag coefficient scales roughly as Re^(-0.2) for turbulent flow (or Re^-0.5 for laminar). Re ∝ velocity. So C_D ~ V^(-0.2) roughly. Let me check. Let me look deeper at the C_D dependence to model it. Let me fit C_D vs velocity and aoa. Actually, let me think about the whole approach. The model is FIXED (PointNet). I cannot change architecture. But I can change: - Input normalization (feat_mean, feat_std) - Target normalization (targ_mean, targ_std) - Loss function - Augmentation - Training schedule - Model selection The critical issue: **the PointNet must extrapolate in velocity for C_D**. How can I help a neural network extrapolate? This is fundamentally hard for NNs. Options: 1. **Feature engineering within the 4 input channels**: I cannot change in_dim (it's 4, fixed by architecture). The 4 channels are x, y, velocity, aoa. But I CAN transform what values go into those channels via feat_mean/feat_std , but that's just affine scaling, which doesn't change extrapolation fundamentally. Wait, actually the standardization is affine: (x - mean)/std. That's linear, so it doesn't change the network's ability to extrapolate in a nonlinear way. Hmm, but wait , do I have to use simple mean/std standardization? The checkpoint format requires feat_mean, feat_std as length-4 tensors, and the inference does `(points - feat_mean)/feat_std`. So the transform applied at inference is FIXED to be affine per-channel. I can choose the mean/std values but it's still affine. So I cannot do a log transform on velocity via the standardization. The input to the network is affine in velocity. BUT , within training I control the data. Could I transform the velocity channel nonlinearly before computing stats and feeding to the network? No , at inference time, the harness loads `points` (raw) and applies `(points - feat_mean)/feat_std`. I don't control inference preprocessing beyond the affine mean/std. So whatever nonlinear transform I want must be... no, I can't. Wait, let me re-read: "the de-standardized [C_D, C_L] predictions are read straight from the model output." and "feat_mean, feat_std: length-4 tensors that standardize the point features before the forward pass." So inference is: `x = (points - feat_mean)/feat_std; out = model(x); pred = out * targ_std + targ_mean`. This is fixed. I only control the 4 tensors + state_dict. Therefore the network sees an affine function of raw velocity. The network's job is to extrapolate. NNs with ReLU/GELU extrapolate roughly linearly outside the training domain (the last active linear pieces continue). Actually GELU networks tend toward linear extrapolation in the outer regions. So if the true relationship C_D vs velocity is monotonic decreasing and roughly linear-ish or can be approximated linearly in the extrapolation region, the network might extrapolate OK if it learns a clean monotonic trend. The danger: the network could learn a non-monotonic or overfi …[truncated]

Clear picture emerging. **C_L** depends almost entirely on angle of attack (corr 0.94) and is essentially velocity-independent, so it should extrapolate well. **C_D** depends on aoa (corr 0.86 with aoa²) *and* has a consistent negative dependence on velocity within every aoa bin , the classic Reynolds-number drag reduction. This velocity→C_D trend is the main extrapolation risk. Let me characterize it precisely and check whether airfoil shapes repeat across velocities.

bash

cd /app && python -c "
import torch, numpy as np
tr = torch.load('data/train/split.pt', weights_only=True)
pts, tg = tr['points'], tr['targets']
vel = pts[:,0,2].numpy(); aoa = pts[:,0,3].numpy()
cd = tg[:,0].numpy(); cl = tg[:,1].numpy()
# identify unique shapes by hashing the x,y geometry
geo = pts[:,:,:2].reshape(pts.shape[0],-1).numpy()
geo_r = np.round(geo,5)
import hashlib
hashes = [hashlib.md5(g.tobytes()).hexdigest() for g in geo_r]
uniq = set(hashes)
print('n cases=',len(hashes),' unique geometries=',len(uniq))
# distribution of how many cases per shape
from collections import Counter
c = Counter(hashes)
counts = np.array(sorted(c.values()))
print('cases-per-shape: min',counts.min(),'max',counts.max(),'mean',counts.mean())
# For shapes with multiple cases, check vel spread
multi = [h for h,n in c.items() if n>=3]
print('shapes with >=3 cases:', len(multi))
"

output

n cases= 630  unique geometries= 630
cases-per-shape: min 1 max 1 mean 1.0
shapes with >=3 cases: 0

Every case has a unique geometry. So no repeated airfoils at different velocities. This is the AirFRANS "scarce"/full task where each (shape, aoa, velocity) is sampled independently. So I can't isolate the pure velocity effect by holding shape+aoa fixed. But I can do a multivariate fit. Let me fit a model C_D ~ f(aoa, velocity, shape-features) to understand the velocity dependence, controlling for aoa. Actually, a cleaner approach: fit C_D as a function of aoa and velocity using a flexible regression, and examine the partial dependence on velocity. Let me do a regression with features [1, aoa, aoa^2, log(vel), aoa*log(vel)] etc. Let me think about the physical model. For an airfoil: - C_L ≈ 2π·(aoa - aoa_0) (thin airfoil theory), roughly linear in aoa, independent of Re. - C_D = C_D_friction + C_D_pressure. Friction drag coefficient for turbulent BL ~ Re^(-1/5). Re = V·c/ν. So C_D_friction ∝ V^(-0.2). Pressure/form drag also depends weakly on Re. So C_D should decrease with V like a power law V^(-0.2) (mild). Over the training range V=31→75 (factor 2.4), V^(-0.2) changes by 2.4^(-0.2)=0.838, ~16% decrease. Over test V=75→81, small additional change. Let me fit to see the actual exponent. Let me regress log(C_D) on log(V) controlling for aoa. Actually C_D also has the induced/pressure drag that grows with aoa^2. Let me do: C_D = (a + b*aoa + c*aoa^2) * V^p, or log(C_D) = log(a+b*aoa+c*aoa^2) + p*log(V). Simplest: multiple linear regression of log(cd) on [1, aoa, aoa^2, log(vel)]. Let me check how well log(vel) explains residual variance and the sign/magnitude of coefficient.

Every geometry is unique , no repeated airfoils, so I can't isolate the velocity effect by holding shape fixed. I'll characterize it with a multivariate regression to find the physical scaling law of C_D with velocity.

bash

cd /app && python -c "
import torch, numpy as np
tr = torch.load('data/train/split.pt', weights_only=True)
pts, tg = tr['points'], tr['targets']
vel = pts[:,0,2].numpy(); aoa = pts[:,0,3].numpy()
cd = tg[:,0].numpy(); cl = tg[:,1].numpy()
lcd = np.log(cd); lv = np.log(vel)
# Regress log(cd) on [1, aoa, aoa^2, log(vel)]
X = np.stack([np.ones_like(aoa), aoa, aoa**2, lv], axis=1)
beta, res, *_ = np.linalg.lstsq(X, lcd, rcond=None)
pred = X@beta
ss = 1 - ((lcd-pred)**2).sum()/((lcd-lcd.mean())**2).sum()
print('log-CD model coefs [1,aoa,aoa^2,log(vel)]:', np.round(beta,4))
print('  -> velocity exponent p =', round(beta[-1],4), ' R2=', round(ss,4))
# add log(vel)*aoa interaction
X2 = np.stack([np.ones_like(aoa), aoa, aoa**2, lv, lv*aoa, lv*aoa**2], axis=1)
beta2,*_ = np.linalg.lstsq(X2, lcd, rcond=None); pred2=X2@beta2
ss2 = 1-((lcd-pred2)**2).sum()/((lcd-lcd.mean())**2).sum()
print('with interactions R2=', round(ss2,4), 'coefs', np.round(beta2,4))
# Now C_L model: linear in aoa
Xl = np.stack([np.ones_like(aoa), aoa, lv], axis=1)
bl,*_ = np.linalg.lstsq(Xl, cl, rcond=None); pl=Xl@bl
ssl = 1-((cl-pl)**2).sum()/((cl-cl.mean())**2).sum()
print()
print('C_L ~ [1,aoa,log(vel)] coefs:', np.round(bl,4), 'R2=',round(ssl,4))
print('  velocity coef for C_L:', round(bl[-1],5),'(near 0 => vel-independent)')
"

output

log-CD model coefs [1,aoa,aoa^2,log(vel)]: [-3.8419  0.3852 14.6366 -0.2106]
  -> velocity exponent p = -0.2106  R2= 0.8178
with interactions R2= 0.8196 coefs [-4.0026 -1.7473 35.9617 -0.17    0.543  -5.4129]

C_L ~ [1,aoa,log(vel)] coefs: [ 0.295   5.6824 -0.0176] R2= 0.8899
  velocity coef for C_L: -0.01764 (near 0 => vel-independent)

Beautiful. The physics confirms: - **C_D ∝ V^(-0.21)** , very close to the classic turbulent skin-friction scaling V^(-0.2)! The velocity exponent is -0.2106, R²=0.82 with just [1, aoa, aoa², log(vel)]. This is a clean, physically-grounded power law. - **C_L** velocity coefficient is -0.0176 (basically 0 relative to the aoa coefficient of 5.68). So C_L is velocity-independent as expected. So the extrapolation story is: - For the test set at V≈78 vs training mean V≈53: C_D should scale by (78/53)^(-0.21) = 0.924, i.e. about 8% lower than a similar case at training mean velocity. Relative to the top of training range (75), (78/75)^-0.21 ≈ 0.992, barely any change. So actually the test velocities (75-81) are only slightly above the training max (75), and the C_D change from the edge of training to the test is small (~1-3%). Wait, the test velocity range is 75-81.6, training is 31-75. So the test is just beyond the upper edge. The C_D power law V^-0.21 is smooth and monotonic, so extrapolating just 8% beyond the range (75→81) is a mild extrapolation of ~1.8% in C_D. That's manageable IF the network learns the smooth monotonic trend rather than overfitting. The real question: will the fixed PointNet, trained on V∈[31,75], produce sensible C_D at V∈[75,81]? Key risk: Neural networks with standardized inputs can behave unpredictably outside the training range, but GELU MLPs tend to extrapolate roughly linearly. Since log(CD) is linear in log(V) with slope -0.21, and CD is a mild function of V, a locally-linear extrapolation in V is fine. The network takes V (affinely scaled) as input, not log(V). But over the narrow test range, linear ≈ power-law locally, so it should be OK. Let me think about what could go wrong and how to maximize performance: 1. **Target normalization for C_D**: C_D ranges 0.007-0.046, very skewed (most small, few large). The std is 0.005. NRMSE on C_D: if normalized by std (0.005), then small absolute errors matter a lot. Predicting C_D well requires capturing both the aoa² growth and the velocity trend. Log-transform of C_D would help the network treat it multiplicatively, but I can't log-transform via the affine targ_mean/targ_std... Wait , targ_mean/targ_std map model output back to physical units via `pred = out*targ_std + targ_mean`. That's affine, fixed. So I train the network to output standardized C_D (affine). I can't make the network output log(C_D) because then de-standardization would give log(C_D) not C_D. Unless... the network outputs are mapped by affine only. So the network must directly predict C_D in (affinely scaled) physical space. OK. Hmm, but actually I could choose targ_mean/targ_std to be something other than the mean/std. But it's still affine. The network output channel for C_D, after affine de-standardization, must equal C_D. So the network learns C_D directly. Fine. 2. **The loss function**: The default is MSE on standardized targets. With standardization by std, MSE on C_D (std 0.005) and C_L (std 0.58) are put on comparable footing (both ~unit variance). That's reasonable for mean_nrmse which normalizes each target. But I should make sure the loss balances the two targets the way the metric does. Actually, let me reconsider the metric. mean_nrmse = (NRMSE_CD + NRMSE_CL)/2 where NRMSE_t = sqrt(mean((pred-true)^2)) / norm_t. If norm_t = std of true targets in eval set, then training with MSE on std-standardized targets directly optimizes the sum of (MSE_CD/var_CD + MSE_CL/var_CL), which is proportional to NRMSE². That aligns well. Good. But I don't know the exact normalizer. Could be range (max-min) or std or RMS of true values. Regardless, standardizing per-target and weighting equally is the robust choice. I might slightly upweight C_D since it's the harder extrapolation and there's a secondary metric cd_nmae. 3. **Improving C_D prediction**: Since C_D has a clean physical form C_D ≈ exp(poly(aoa)) · V^(-0.21), and the network must learn this from (shape, aoa, V), the …[truncated]

Excellent , this is the key finding. The data obeys a clean physical law: - **C_D ∝ V^(−0.21)** (R²=0.82 with just aoa, aoa², log V) , almost exactly the textbook turbulent skin-friction scaling V^(−0.2). - **C_L is velocity-independent** (velocity coefficient −0.018 vs. aoa coefficient 5.68). This is powerful: I can use it to **physically augment** training data into the high-velocity test regime, turning extrapolation into interpolation. But first let me verify the exponent is stable, and set up a *pseudo-extrapolation* validation (hold out the highest-velocity training cases) since the real `val` split shares the training velocity range and won't measure extrapolation.

bash

cd /app && python -c "
import torch, numpy as np
tr = torch.load('data/train/split.pt', weights_only=True)
pts, tg = tr['points'], tr['targets']
vel = pts[:,0,2].numpy(); aoa = pts[:,0,3].numpy()
cd = tg[:,0].numpy(); lcd=np.log(cd); lv=np.log(vel)
# Fit exponent using ONLY low-velocity cases, then see if it predicts high-vel CD
for thr in [60, 65, 68]:
    lo = vel<thr; hi = vel>=thr
    X = np.stack([np.ones(lo.sum()), aoa[lo], aoa[lo]**2, lv[lo]],1)
    b,*_ = np.linalg.lstsq(X, lcd[lo], rcond=None)
    Xh = np.stack([np.ones(hi.sum()), aoa[hi], aoa[hi]**2, lv[hi]],1)
    ph = Xh@b
    r2h = 1-((lcd[hi]-ph)**2).sum()/((lcd[hi]-lcd[hi].mean())**2).sum()
    # also compare to using the low-vel exponent extrapolated
    print(f'thr={thr}: train on V<{thr} (n={lo.sum()}), exponent={b[-1]:.3f}, R2 on V>={thr} (n={hi.sum()})={r2h:.3f}')
"

output

thr=60: train on V<60 (n=413), exponent=-0.184, R2 on V>=60 (n=217)=0.858
thr=65: train on V<65 (n=482), exponent=-0.222, R2 on V>=65 (n=148)=0.847
thr=68: train on V<68 (n=535), exponent=-0.228, R2 on V>=68 (n=95)=0.815

The exponent is stable (-0.18 to -0.23) and the fit trained only on low-velocity cases predicts high-velocity C_D well (R²≈0.82-0.86). This strongly confirms: 1. The power law extrapolates well. 2. An exponent around -0.2 is robust. So my physics-informed augmentation using CD' = CD·(V'/V)^(-0.2) is well-justified. I'll use p ≈ -0.20 (could even slightly tune, but -0.2 is physically principled and empirically stable). Now let me set up the full pipeline. Let me first check available compute (GPU?), torch version, and baseline training time.

The exponent is stable (−0.18 to −0.23) and a fit trained only on low-velocity cases predicts high-velocity C_D with R²≈0.85. This confirms the power law extrapolates reliably. Let me check the compute environment before building the pipeline.

bash

cd /app && python -c "
import torch, time
print('torch', torch.__version__, 'cuda', torch.cuda.is_available())
print('cpu threads', torch.get_num_threads())
" && nproc && free -h | head -2

output

torch 2.5.1+cu124 cuda False
cpu threads 16
16
               total        used        free      shared  buff/cache   available
Mem:            69Gi        70Mi        68Gi          0B        29Mi        68Gi

CPU only, 16 threads, 68GB RAM. The dataset is small (630 cases, 256 points, 4 dims). Training a PointNet on this is fast even on CPU. Let me verify baseline training speed by running the provided train.py quickly with few epochs. Actually, let me first design my validation protocol properly. I'll create a pseudo-extrapolation split: train on cases with V below some threshold, validate on cases with V above it, to measure extrapolation performance. This mirrors the real task. Let me now think about the full training design: 1. **Normalization**: - feat_mean/feat_std: standardize x, y, velocity, aoa. For x,y use their stats. For velocity and aoa, standardize by train stats. - Important consideration for extrapolation: Should I standardize velocity using a mean/std that makes the test range not too far out? With augmentation covering high velocities, the effective training range expands, so standardization is less critical. I'll compute feat_mean/feat_std over the AUGMENTED training distribution so the network sees a sensible scale. Actually, the feat_mean/feat_std are applied at inference to raw points. They just need to be consistent with training. I'll compute them from the augmented data (or original; but augmented expands velocity range so std bigger). Let me use stats that reflect the full velocity range I train on including augmentation, so test velocities map to reasonable standardized values. 2. **Target normalization**: standardize C_D, C_L by mean/std. Consider the heavy skew of C_D. Since I must output physical C_D via affine map, I can't log-transform. But I can still help by weighting the loss. Let me think about whether to optimize MSE in standardized space (good for NRMSE-by-std) vs relative error (good for cd_nmae). Actually, I realize I could be clever: even though the final de-standardization is affine, I can train the network to predict C_D in standardized space and use a loss that emphasizes relative accuracy. But simplest robust approach: standardized MSE + maybe a relative term for C_D. Let me not overcomplicate; I'll consider Huber or a combination. 3. **Loss**: Train to minimize standardized MSE (aligns with mean_nrmse if normalizer is std). Possibly add small weight to C_D. Let me make the loss = w_cd * MSE_cd_std + w_cl * MSE_cl_std, tune w. 4. **Augmentation**: - Velocity rescaling with CD power-law adjustment, CL unchanged. Sample a target velocity from a range that covers test (e.g., up to 90-95). - Mild geometry jitter (small Gaussian noise on x,y). - Possibly aoa jitter with corresponding CL/CD adjustment? Risky; skip or keep tiny. Actually CL ≈ a + b*aoa, CD ≈ exp(poly(aoa)). I could augment aoa but the relationship is shape-dependent. Safer to only augment velocity (clean law) and jitter geometry slightly. 5. **Model selection**: Use the pseudo-extrapolation validation to pick hyperparameters and epochs. For the final model, train on ALL data (train + maybe val) with augmentation. 6. **Ensembling**: The task allows it? "you are free to change everything about how you train ... model selection ... or ensembling." But the checkpoint is a single state_dict loaded into one model. So I can't ensemble multiple networks at inference (only one forward pass). Unless I ensemble into weight averaging (SWA) , averaging weights of models from different epochs/runs into one state_dict. SWA is allowed since it's a single state_dict. That could help generalization. Let me keep that as an option. Let me build the pipeline incrementally. First, a robust training script with a function I can call with different configs for the pseudo-extrapolation experiment. Let me write a module `lib.py` with the core training + eval, then experiment. Let me define the evaluation metric. I'll compute NRMSE per target normalized by the std of true targets, and mean. Since I don't know the exact normalizer, I'll also report raw RMSE and MAE. For the pseudo-extrapolation holdout, the normalizer sh …[truncated]

CPU-only with 16 threads and a tiny dataset , training will be fast. Now I'll build a reusable library with the model training, a physics-informed velocity augmentation, and a **pseudo-extrapolation validation** (train on low-velocity cases, test on the held-out highest-velocity cases) to verify the core idea before committing.

bash

cd /app && python -c "
import torch, numpy as np
# Check how CL varies with velocity more carefully via partial residuals, and CD residual after aoa
tr = torch.load('data/train/split.pt', weights_only=True)
pts, tg = tr['points'], tr['targets']
vel = pts[:,0,2].numpy(); aoa = pts[:,0,3].numpy()
cd = tg[:,0].numpy(); cl = tg[:,1].numpy()
# Fit CL ~ cubic in aoa, check residual vs velocity
Xa = np.stack([aoa**k for k in range(4)],1)
bl,*_=np.linalg.lstsq(Xa,cl,rcond=None); rl=cl-Xa@bl
print('CL: corr(resid_after_aoa_cubic, vel)=', np.corrcoef(rl,vel)[0,1], 'resid std', rl.std(), 'vs CL std', cl.std())
# So velocity explains essentially nothing extra for CL
# CD: log model residual vs shape proxies (thickness)
yloc = pts[:,:,1].numpy()
thick = yloc.max(1)-yloc.min(1)  # max thickness proxy
lcd=np.log(cd); lv=np.log(vel)
X=np.stack([np.ones_like(aoa),aoa,aoa**2,lv],1)
b,*_=np.linalg.lstsq(X,lcd,rcond=None); r=lcd-X@b
print('CD: corr(log-resid, thickness)=', np.corrcoef(r,thick)[0,1])
print('thickness range', thick.min(), thick.max())
"

output

CL: corr(resid_after_aoa_cubic, vel)= -0.020116020990871914 resid std 0.18913952 vs CL std 0.5757378
CD: corr(log-resid, thickness)= 0.4911331672413428
thickness range 0.0509462 0.2018896

Confirmed: - CL: after removing aoa (cubic), velocity explains nothing (corr -0.02). Residual std 0.19 , that's the shape-dependent part of CL (camber etc.), which the PointNet can capture from geometry. - CD: after the log model, thickness correlates 0.49 with residual , so thicker airfoils have higher drag, as expected. The PointNet can learn this from geometry. So the physical picture is fully consistent. My plan is solid. Let me build the training library. Key design decisions finalized: - **Velocity augmentation**: rescale V → V·r where r drawn to cover test range; adjust CD → CD·r^(-0.2), keep CL fixed. - **Geometry jitter**: small noise on x,y. - **Loss**: standardized MSE, possibly weighted. - **Normalization**: compute feat stats over augmented range. - **Validation**: pseudo-extrapolation (train V<thr, val V>=thr) + real val. Let me write lib.py with flexible training.

Both targets' physics are fully consistent: C_L's shape-dependent residual (std 0.19) and C_D's thickness dependence (corr 0.49) are geometric signals the PointNet can learn, while velocity acts through the clean V^−0.2 law on C_D only. Now let me write a reusable training library.

str_replace_based_edit_tool

command: create
path: /app/lib.py
file_text: """Shared training/eval utilities for the airfoil force-coefficient task."""
from __future__ import annotations

import json
import os
from pathlib import Path

import numpy as np
import torch
import torch.nn.functional as F

APP_DIR = Path(os.environ.get("APP_DIR", "/app"))
import sys
sys.path.insert(0, str(APP_DIR))
from candidate_model import build_model  # noqa: E402

# indices into the 4 input channels
X_I, Y_I, V_I, A_I = 0, 1, 2, 3
CD_I, CL_I = 0, 1
CD_VEL_EXPONENT = -0.20  # C_D ~ V^p (turbulent skin-friction scaling, empirically -0.21)


def load_cfg():
    with (APP_DIR / "task_config.json").open() as fh:
        return json.load(fh)


def load_split(name):
    p = torch.load(APP_DIR / "data" / name / "split.pt", map_location="cpu", weights_only=True)
    pts = p["points"].float()
    tg = p["targets"].float() if "targets" in p else None
    return pts, tg


def velocity_augment(pts, tg, vmin, vmax, p=CD_VEL_EXPONENT, gen=None):
    """Rescale each case's inlet velocity to a random target in [vmin,vmax];
    adjust C_D by the power law V^p and keep C_L unchanged."""
    gen = gen or torch.Generator().manual_seed(0)
    n = pts.shape[0]
    v_old = pts[:, 0, V_I].clone()
    v_new = vmin + (vmax - vmin) * torch.rand(n, generator=gen)
    ratio = v_new / v_old
    pts2 = pts.clone()
    pts2[:, :, V_I] = v_new.view(-1, 1)
    tg2 = tg.clone()
    tg2[:, CD_I] = tg[:, CD_I] * ratio.pow(p)
    return pts2, tg2


def standardize_fit(pts, tg):
    flat = pts.reshape(-1, pts.shape[-1])
    fmean = flat.mean(0)
    fstd = flat.std(0).clamp_min(1e-8)
    tmean = tg.mean(0)
    tstd = tg.std(0).clamp_min(1e-8)
    return fmean, fstd, tmean, tstd


def nrmse_report(pred, true):
    """Per-target metrics. NRMSE normalized by std of true targets (on this set)."""
    err = pred - true
    rmse = err.pow(2).mean(0).sqrt()
    mae = err.abs().mean(0)
    std = true.std(0).clamp_min(1e-12)
    mean_abs = true.abs().mean(0).clamp_min(1e-12)
    rng = (true.max(0).values - true.min(0).values).clamp_min(1e-12)
    nrmse_std = rmse / std
    nrmse_rng = rmse / rng
    nmae_mean = mae / mean_abs
    return {
        "rmse": rmse.tolist(),
        "mae": mae.tolist(),
        "nrmse_std": nrmse_std.tolist(),
        "mean_nrmse_std": nrmse_std.mean().item(),
        "worst_nrmse_std": nrmse_std.max().item(),
        "nrmse_rng": nrmse_rng.tolist(),
        "mean_nrmse_rng": nrmse_rng.mean().item(),
        "cd_nmae_mean": nmae_mean[CD_I].item(),
    }


def predict(model, pts, fmean, fstd, tmean, tstd, device="cpu", bs=256):
    model.eval()
    outs = []
    with torch.no_grad():
        for i in range(0, pts.shape[0], bs):
            xb = ((pts[i:i+bs] - fmean.view(1, 1, -1)) / fstd.view(1, 1, -1)).to(device)
            o = model(xb).cpu()
            outs.append(o * tstd.view(1, -1) + tmean.view(1, -1))
    return torch.cat(outs, 0)


def train_model(
    train_pts, train_tg, cfg,
    epochs=120, bs=32, lr=1e-3, wd=1e-4,
    aug_vmax=None, aug_vmin=None, aug_p=CD_VEL_EXPONENT,
    jitter=0.0, w_cd=1.0, w_cl=1.0, seed=0,
    fmean=None, fstd=None, tmean=None, tstd=None,
    val_pts=None, val_tg=None, swa_start=None, verbose=False,
    loss_type="mse", huber_delta=1.0,
):
    torch.manual_seed(seed)
    np.random.seed(seed)
    gen = torch.Generator().manual_seed(seed)
    device = "cpu"

    # Build the velocity-augmented pool once to fit normalization over the wide range.
    if fmean is None:
        if aug_vmax is not None:
            ap, at = velocity_augment(train_pts, train_tg, aug_vmin, aug_vmax, aug_p, gen)
            pool_pts = torch.cat([train_pts, ap], 0)
            pool_tg = torch.cat([train_tg, at], 0)
        else:
            pool_pts, pool_tg = train_pts, train_tg
        fmean, fstd, tmean, tstd = standardize_fit(pool_pts, pool_tg)

    model = build_model(cfg).to(device)
    opt = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=wd)
    sched = torch.optim.lr_sc
…[truncated]

output

New file created successfully at /app/lib.py

Now let me run the pseudo-extrapolation experiment. I'll split the training data: train on V < 65, hold out V >= 65 as pseudo-test (mimicking the extrapolation gap). Then compare: - (a) baseline: no augmentation - (b) velocity augmentation covering the held-out range This validates whether augmentation helps extrapolation. Let me write an experiment script.

Now let me run the pivotal experiment: split training data at V<65 (train) vs V≥65 (pseudo-extrapolation test) and compare no-augmentation vs. velocity-augmentation.

bash

cd /app && cat > /tmp/exp1.py << 'EOF'
import torch, numpy as np, time
import lib

cfg = lib.load_cfg()
pts, tg = lib.load_split("train")
vel = pts[:,0,2]
thr = 65.0
tr_idx = (vel < thr).nonzero().squeeze(1)
te_idx = (vel >= thr).nonzero().squeeze(1)
tp, tt = pts[tr_idx], tg[tr_idx]
ep, et = pts[te_idx], tg[te_idx]
print(f"pseudo-split: train n={len(tr_idx)} (V<{thr}), test n={len(te_idx)} (V>={thr})")
print(f"test V range {ep[:,0,2].min():.1f}-{ep[:,0,2].max():.1f}")

def run(tag, **kw):
    t0=time.time()
    model, norms = lib.train_model(tp, tt, cfg, seed=0, **kw)
    pr = lib.predict(model, ep, *norms)
    r = lib.nrmse_report(pr, et)
    print(f"[{tag}] mean_nrmse_std={r['mean_nrmse_std']:.4f} cd={r['nrmse_std'][0]:.4f} cl={r['nrmse_std'][1]:.4f} "
          f"cd_rmse={r['rmse'][0]:.5f} ({time.time()-t0:.0f}s)")
    return r

run("baseline no-aug", epochs=80, aug_vmax=None)
run("vel-aug up to 85", epochs=80, aug_vmin=45, aug_vmax=85)
EOF
python /tmp/exp1.py

output

[stderr]
Traceback (most recent call last):
  File "/tmp/exp1.py", line 2, in <module>
    import lib
ModuleNotFoundError: No module named 'lib'

bash

cd /app && sed -i '1i import sys; sys.path.insert(0, "/app")' /tmp/exp1.py && python /tmp/exp1.py

output

pseudo-split: train n=482 (V<65.0), test n=148 (V>=65.0)
test V range 65.0-74.9
[baseline no-aug] mean_nrmse_std=0.1996 cd=0.2605 cl=0.1387 cd_rmse=0.00109 (46s)
[vel-aug up to 85] mean_nrmse_std=0.2078 cd=0.3779 cl=0.0376 cd_rmse=0.00158 (64s)

Interesting and important result! The velocity augmentation HURT C_D (cd nrmse went from 0.26 to 0.38) but HELPED C_L a lot (0.14 → 0.038). Wait, that's surprising for C_D. Let me think. The augmentation rescales velocity and adjusts C_D by V^-0.2. But the pseudo-test here is V in [65,75], which is WITHIN the augmentation range [45,85]. So why did C_D get worse? Hmm. Possible issues: 1. The normalization: when I augment up to 85, the velocity std increases, so the standardized velocity signal for the real cases is compressed. This might make it harder for the network to use velocity precisely. 2. The augmentation exponent -0.2 might differ from the true local behavior, adding noise to C_D targets. 3. The baseline for C_D in pseudo-test (nrmse 0.26) , the test is V in [65,75], just above train max 65. The baseline extrapolates poorly on C_L (0.14) but augmentation fixes C_L. Actually wait , why would C_D augmentation hurt? Let me reconsider. The issue might be that by adding many augmented cases with scaled velocities, I'm diluting the real data and the network focuses less on getting the absolute C_D right. Or the power law I apply introduces systematic error. Actually, let me reconsider. The C_D nrmse of 0.26 (baseline) on the pseudo-test corresponds to cd_rmse 0.00109. That's already decent. The augmentation made it 0.00158. Let me reconsider whether C_D really needs velocity augmentation. The test velocities (real: 75-81) are just slightly beyond train max (75). C_D changes by ~(81/75)^-0.2 ≈ 0.985, 1.5%. So the absolute C_D extrapolation error from velocity is tiny. The baseline network, if it learns a smooth function, will extrapolate C_D fine for such a small velocity step. But C_L , the baseline extrapolates C_L poorly (nrmse 0.14 on pseudo-test). Why? C_L doesn't depend on velocity, so why does the baseline do worse on high-velocity C_L? Because the network might erroneously couple velocity into C_L prediction. When velocity goes out of range, the network's C_L output drifts because it learned some spurious velocity dependence. The augmentation (keeping C_L fixed across velocities) teaches the network that C_L is velocity-invariant, dramatically improving C_L extrapolation (0.14 → 0.038)! That's a big win for C_L. So the ideal: augment to fix C_L's velocity-invariance, but DON'T corrupt C_D. The C_D degradation from augmentation , let me investigate. Maybe the problem is the exponent or the normalization. Let me try: - Augment but with the CORRECT per-case behavior. Actually, maybe I should augment velocity for the C_L-invariance benefit, but the C_D power law adjustment adds noise. Let me test: what if the augmentation range is narrower, or what if I tune the exponent? Actually, let me reconsider the C_D degradation more carefully. Let me check: is it the normalization change (wider velocity std) or the augmented C_D targets themselves? Let me run an ablation: - vel-aug but compute normalization from ORIGINAL data only (not augmented pool). - vel-aug with different exponents. - vel-aug with narrower range [55, 80]. Also, let me reconsider: maybe the real issue is that augmentation with a fixed global exponent -0.2 is too simplistic for C_D, because C_D's velocity sensitivity might vary by case. Whereas for C_L, keeping it constant is exactly right. Alternative idea: **Decouple the augmentation**. What if I only rely on the network for C_D (small extrapolation, baseline is fine) and use augmentation primarily to stabilize C_L? But it's one network outputting both. Hmm, but the augmented C_D targets are still "mostly right" (power law is good, R²~0.85). So why worse? Let me look at whether baseline C_D is genuinely good or just lucky on this split. Let me also test on a more extreme split (the real test is BEYOND train max, not within). Actually the key realization: my pseudo-test (V≥65) is WITHIN the augmentation range, so augmentation should help, not hurt, if the power law were perfect. The fact tha …[truncated]

Very informative asymmetry: velocity augmentation **dramatically helps C_L extrapolation** (nrmse 0.14→0.038) because it teaches velocity-invariance, but it **hurt C_D** (0.26→0.38). The C_L win makes sense , the baseline spuriously couples velocity into C_L, and augmentation removes that. I need to keep the C_L benefit without corrupting C_D. Let me diagnose the C_D degradation with targeted ablations.

bash

cd /app && cat > /tmp/exp2.py << 'EOF'
import sys; sys.path.insert(0,"/app")
import torch, numpy as np, time
import lib
cfg = lib.load_cfg()
pts, tg = lib.load_split("train")
vel = pts[:,0,2]
thr=65.0
tp,tt = pts[vel<thr], tg[vel<thr]
ep,et = pts[vel>=thr], tg[vel>=thr]

def run(tag, **kw):
    t0=time.time()
    model, norms = lib.train_model(tp, tt, cfg, seed=0, **kw)
    pr = lib.predict(model, ep, *norms)
    r = lib.nrmse_report(pr, et)
    print(f"[{tag:28s}] mean={r['mean_nrmse_std']:.4f} cd={r['nrmse_std'][0]:.4f} cl={r['nrmse_std'][1]:.4f} cd_rmse={r['rmse'][0]:.5f} ({time.time()-t0:.0f}s)")
    return r

# Fit normalization from original only, but still augment during training
fm,fs,tm,ts = lib.standardize_fit(tp, tt)
run("aug[45,85] norm=orig", epochs=80, aug_vmin=45, aug_vmax=85, fmean=fm,fstd=fs,tmean=tm,tstd=ts)
run("aug[55,80] exp-0.2", epochs=80, aug_vmin=55, aug_vmax=80)
run("aug[45,85] exp-0.25", epochs=80, aug_vmin=45, aug_vmax=85, aug_p=-0.25)
run("aug cl-only (exp0 cd)", epochs=80, aug_vmin=45, aug_vmax=85, aug_p=0.0)
EOF
python /tmp/exp2.py

output

[stderr]
Shell command timed out.
Tool interaction exceeded 300 seconds.

bash

restart: true

output

<system>tool has been restarted.</system>

Each run takes ~60-80s with 80 epochs. 4 runs = too long for 300s. Let me run experiments in the background and poll, or run fewer/faster. Let me reduce epochs and run in background with output to a file. Let me run experiments in background using nohup and poll.

Each run takes ~60-80s, so I'll run experiments in the background and poll the results file rather than blocking.

bash

cd /app && cat > /tmp/exp2.py << 'EOF'
import sys; sys.path.insert(0,"/app")
import torch, time
import lib
cfg = lib.load_cfg()
pts, tg = lib.load_split("train")
vel = pts[:,0,2]
thr=65.0
tp,tt = pts[vel<thr], tg[vel<thr]
ep,et = pts[vel>=thr], tg[vel>=thr]
def run(tag, **kw):
    t0=time.time()
    model, norms = lib.train_model(tp, tt, cfg, seed=0, **kw)
    r = lib.nrmse_report(lib.predict(model, ep, *norms), et)
    print(f"[{tag:26s}] mean={r['mean_nrmse_std']:.4f} cd={r['nrmse_std'][0]:.4f} cl={r['nrmse_std'][1]:.4f} cd_rmse={r['rmse'][0]:.5f} ({time.time()-t0:.0f}s)",flush=True)
fm,fs,tm,ts = lib.standardize_fit(tp, tt)
run("aug[45,85]norm=orig", epochs=70, aug_vmin=45, aug_vmax=85, fmean=fm,fstd=fs,tmean=tm,tstd=ts)
run("aug[55,80]exp-0.2", epochs=70, aug_vmin=55, aug_vmax=80)
run("aug-clonly(cd exp0)", epochs=70, aug_vmin=45, aug_vmax=85, aug_p=0.0)
run("baseline", epochs=70)
print("DONE",flush=True)
EOF
nohup python /tmp/exp2.py > /tmp/exp2.out 2>&1 &
echo "started PID $!"

output

started PID 455

bash

sleep 240 && cat /tmp/exp2.out

output

[aug[45,85]norm=orig       ] mean=0.2082 cd=0.3376 cl=0.0787 cd_rmse=0.00141 (75s)
[aug[55,80]exp-0.2         ] mean=0.2517 cd=0.4440 cl=0.0594 cd_rmse=0.00185 (55s)
[aug-clonly(cd exp0)       ] mean=0.3183 cd=0.5814 cl=0.0553 cd_rmse=0.00243 (52s)
[baseline                  ] mean=0.2302 cd=0.3206 cl=0.1399 cd_rmse=0.00134 (24s)
DONE

Now I have more data. Note the baseline here got cd=0.32 (vs 0.26 in the first run) , there's run-to-run variance (different seed behavior / the first exp1 used epochs=80). Let me be careful about variance. But patterns: - C_L: all augmentation variants massively improve C_L (0.14 baseline → 0.055-0.079). Great. - C_D: augmentation consistently hurts C_D. aug-clonly (cd exp0, i.e., keep C_D constant across velocity) is WORST for C_D (0.58). aug with exp-0.2 gives cd 0.34-0.44. Baseline cd 0.32. So the more I perturb C_D via augmentation, the worse C_D extrapolation gets on this pseudo-test. And the pseudo-test is V∈[65,75], within the augmentation range. Why does velocity augmentation hurt C_D so much? Let me think differently. Key insight: The pseudo-test cases are REAL cases at V∈[65,75]. Their true C_D follows the real physics. My augmentation creates synthetic cases by taking low-V cases and scaling. But here's the subtle issue: **the augmented cases at V∈[65,85] are derived from DIFFERENT airfoils than the real test cases**, and more importantly, **augmentation dilutes the signal**: for a given real training airfoil at V=40 with its true C_D, I create copies at V=45..85 with power-law-scaled C_D. The network now must fit a function that, for that airfoil's geometry, gives C_D following the power law exactly. But different airfoils in reality might have slightly different velocity sensitivity, and more importantly the network has to simultaneously fit geometry→C_D AND velocity→C_D. Hmm, but that should be fine. Let me reconsider. Actually , I wonder if the issue is the **absolute scale / target normalization under augmentation**. When I augment, the C_D distribution changes (more low-C_D cases from high-V scaling), shifting tmean/tstd. With norm=orig (first row) C_D improved to 0.34 from 0.44. So normalization matters somewhat. But fundamentally, augmentation is hurting C_D even within-range. This is counterintuitive. Let me reconsider whether the baseline C_D extrapolation is actually a problem at all. Real situation: test V∈[75,81], train V∈[31,75]. The C_D extrapolation needed is tiny (1-2% in C_D from velocity). The baseline already learns the velocity→C_D trend within [31,75]. Extrapolating to 81 is a tiny step. So maybe for C_D, the baseline is fine and I should NOT augment C_D at all. But for C_L, baseline extrapolates badly (0.14) because of spurious velocity coupling, and augmentation fixes it. So the BEST approach might be: **augment velocity but keep C_L fixed AND keep C_D following its true power law** , but we saw that hurts C_D. Alternatively: **augment for C_L benefit, but find why C_D suffers and mitigate.** Wait. Let me reconsider. Actually maybe the issue is that augmentation changes the *effective weighting* of the loss. When I augment, I double the dataset with velocity-varied copies. The network sees each geometry at many velocities. This is a LOT of emphasis on velocity variation. For C_L, that's good (learn invariance). For C_D, the power law might be slightly wrong per-case, injecting label noise that specifically degrades C_D. Let me test a cleaner idea: **separate augmentation strength / decouple**. What if I keep the augmentation but the C_D target for augmented cases uses the power law, AND I also downweight... no. Let me reconsider from scratch. Alternative approach to fix C_L extrapolation WITHOUT augmentation: **Why does the baseline couple velocity into C_L?** Because the network is free to use velocity. If I could tell the network "C_L doesn't depend on velocity," I'd fix it. Augmentation is one way. Another thought: the degradation of C_D under augmentation might be specific to this pseudo-split where test is within aug range but the REAL benefit is beyond range. Let me reconsider what metric matters. On the REAL test (V 75-81), baseline C_D extrapolation might be worse than it appears here because 75-81 is beyond 75. Let me make the pseudo-test HARDER / truly beyond range: …[truncated]

The pattern is clear and consistent: augmentation **always rescues C_L** (0.14→0.06) but **degrades C_D** , and the more it perturbs C_D (exp 0 = worst), the worse C_D gets. Before deciding, let me probe *how* the baseline vs augmented models actually respond to velocity beyond the training range, using a fixed set of airfoils swept across velocity.

bash

cd /app && cat > /tmp/probe.py << 'EOF'
import sys; sys.path.insert(0,"/app")
import torch, numpy as np
import lib
cfg = lib.load_cfg()
pts, tg = lib.load_split("train")
vel = pts[:,0,2]
thr=65.0
tp,tt = pts[vel<thr], tg[vel<thr]
# take 30 airfoils, sweep velocity 40..90, see predicted CD and CL
sel = pts[vel<thr][:40].clone()
def sweep(model, norms, label):
    print(f"-- {label}: predicted response to velocity (median over 40 airfoils) --")
    cds=[]; cls=[]
    vs=[40,50,60,65,70,75,80,85,90]
    for v in vs:
        s = sel.clone(); s[:,:,2]=v
        pr = lib.predict(model, s, *norms)
        cds.append(pr[:,0].median().item()); cls.append(pr[:,1].median().item())
    print("   V  :", " ".join(f"{v:7d}" for v in vs))
    print("   CD :", " ".join(f"{c:7.5f}" for c in cds))
    print("   CL :", " ".join(f"{c:7.4f}" for c in cls))
    # ratio CD(v)/CD(60) to compare to power law (v/60)^-0.2
    base=cds[vs.index(60)]
    print("   CDr:", " ".join(f"{c/base:7.4f}" for c in cds), "(network)")
    print("   pwr:", " ".join(f"{(v/60)**-0.2:7.4f}" for v in vs), "(V^-0.2)")

m0,n0 = lib.train_model(tp, tt, cfg, seed=0, epochs=70)
sweep(m0,n0,"baseline")
m1,n1 = lib.train_model(tp, tt, cfg, seed=0, epochs=70, aug_vmin=45, aug_vmax=90)
sweep(m1,n1,"vel-aug[45,90]")
print("DONE",flush=True)
EOF
nohup python /tmp/probe.py > /tmp/probe.out 2>&1 &
echo "started $!"

output

started 501

bash

sleep 120 && cat /tmp/probe.out

output

-- baseline: predicted response to velocity (median over 40 airfoils) --
   V  :      40      50      60      65      70      75      80      85      90
   CD : 0.01123 0.01093 0.01051 0.01048 0.01054 0.01057 0.01065 0.01091 0.01144
   CL :  0.4945  0.5379  0.4792  0.4618  0.4719  0.4938  0.5218  0.5563  0.6011
   CDr:  1.0678  1.0398  1.0000  0.9967  1.0022  1.0057  1.0127  1.0376  1.0877 (network)
   pwr:  1.0845  1.0371  1.0000  0.9841  0.9696  0.9564  0.9441  0.9327  0.9221 (V^-0.2)
-- vel-aug[45,90]: predicted response to velocity (median over 40 airfoils) --
   V  :      40      50      60      65      70      75      80      85      90
   CD : 0.01128 0.01079 0.01034 0.01017 0.01001 0.00985 0.00972 0.00964 0.00961
   CL :  0.4223  0.4341  0.4348  0.4361  0.4404  0.4433  0.4394  0.4302  0.4217
   CDr:  1.0900  1.0428  1.0000  0.9831  0.9679  0.9523  0.9393  0.9319  0.9292 (network)
   pwr:  1.0845  1.0371  1.0000  0.9841  0.9696  0.9564  0.9441  0.9327  0.9221 (V^-0.2)
DONE

This is extremely illuminating! **Baseline** (trained on V<65): - C_D: decreases from V=40 to 60 (following physics), but then at V>65 (outside training range) it TURNS AROUND and INCREASES: 0.01048 at 65 → 0.01144 at 90. This is WRONG , C_D should keep decreasing. The network extrapolates with an upward turn (classic NN extrapolation failure , the unconstrained region bends the wrong way). So baseline OVER-predicts C_D at high velocity. - C_L: wobbles and INCREASES strongly beyond range: 0.46 at 65 → 0.60 at 90. Should stay flat (~0.46). Big spurious velocity coupling. **Vel-aug[45,90]** (trained on V<65 with augmentation to V=90): - C_D: cleanly follows the power law V^-0.2 almost exactly (CDr matches pwr row: 0.952 vs 0.956 at 75, 0.929 vs 0.922 at 90). The network learned the correct monotonic decreasing trend and extrapolates correctly. - C_L: stays nearly flat (0.42-0.44 across all velocities). Correct velocity-invariance. So the augmentation makes BOTH C_D and C_L extrapolate in the physically correct direction! But then why did the pseudo-test (V∈[65,75]) show WORSE C_D for augmentation (0.34-0.44) vs baseline (0.32)? Look at the numbers: at V=65-75, baseline C_D is ~0.0105 and barely changes, while augmented C_D is 0.0102→0.0098 (decreasing). The TRUE C_D at V∈[65,75]... the power law says it should be decreasing. So augmented should be MORE correct. But the pseudo-test nrmse said augmented C_D is worse! This is a contradiction. Let me reconcile. The discrepancy must be about the ABSOLUTE LEVEL / calibration, not the velocity trend. Possible explanation: The augmentation changes the overall C_D calibration (bias) because: 1. The augmented dataset has a different C_D distribution → different tmean/tstd → the network fits a slightly different absolute level. 2. More importantly, when pseudo-test is WITHIN the aug range [45,90] but the real improvement is the TREND, the absolute offset might be off. Actually, look carefully: the pseudo-test nrmse for C_D with aug was 0.34-0.44 while baseline 0.32. The difference in cd_rmse: baseline 0.00134, aug 0.00141-0.00185. These are close-ish. And there's seed variance. But the probe shows augmentation gives the correct TREND. The issue is that on this particular pseudo-test (V just above training max of 65, up to 75), the true C_D trend is only mildly decreasing, and the baseline's "flat then up" approximation happens to not be too far off in absolute terms for V=65-75 (since the upturn is mild until V>75). The augmented model follows the steeper power-law decrease which might slightly overshoot for these specific cases if their true exponent is milder. The REAL test is V=75-81, where the baseline's upturn becomes a serious problem (baseline predicts C_D increasing 0.0106→0.0107→ rising, while truth decreases). So for the REAL extrapolation regime, augmentation should win on C_D too. Let me make the pseudo-test match reality better: train on V<68 (most data), test on V>72 , wait there's little data above 72. Let me instead train on V < 58 and test on V > 68, creating a genuine gap and testing truly-beyond-range extrapolation. The real task trains on up to 75 and tests 75-81 (a ~8% step beyond). To mimic "test beyond max," I should train on V<Vc and test on the highest available V, with the test clearly above the training max. Actually the cleanest mimic: train on V < 65, test on V in [72, 75] (the top slice, clearly above... no, 72-75 is just below 75). Hmm, train max is 75 regardless. Let me reframe: the real gap is test_min(75) - train_max(75) ≈ 0, test_max(81) - train_max(75) = 6, i.e., test extends ~8% beyond train max. To mimic: train on V < 69, test on V > 69 (test up to 75, which is 75/69=1.087, ~8.7% beyond train max of 69). That mimics the real 8% extrapolation. Let me use that split. There should be enough test cases. Let me recount: earlier, V<68 gave 535 train / 95 test. So V<69 ~ similar. Good sample. Let me redo the pseudo-extrapolation with train …[truncated]

This is the decisive diagnostic. The baseline **extrapolates C_D the wrong way** , beyond the training range it turns *upward* (0.01048→0.01144 at V=90) when physics demands it keep decreasing, and its C_L spuriously climbs (0.46→0.60). The augmented model tracks the V^−0.2 law almost exactly for C_D and holds C_L flat. The earlier "C_D got worse" was measured on a pseudo-test *within* the range where the baseline's upturn hadn't hurt yet. Let me build a **faithful** mimic of the real gap (train V<69, test V≥69, i.e. test ~9% beyond train max, just like real) and compare.

bash

cd /app && cat > /tmp/exp3.py << 'EOF'
import sys; sys.path.insert(0,"/app")
import torch, time
import lib
cfg = lib.load_cfg()
pts, tg = lib.load_split("train")
vel = pts[:,0,2]
thr=69.0
tp,tt = pts[vel<thr], tg[vel<thr]
ep,et = pts[vel>=thr], tg[vel>=thr]
print(f"train n={len(tp)} (V<{thr}), test n={len(ep)} (V in {ep[:,0,2].min():.1f}-{ep[:,0,2].max():.1f})",flush=True)
def run(tag, reps=2, **kw):
    accs=[]
    for s in range(reps):
        model, norms = lib.train_model(tp, tt, cfg, seed=s, **kw)
        r = lib.nrmse_report(lib.predict(model, ep, *norms), et)
        accs.append((r['mean_nrmse_std'],r['nrmse_std'][0],r['nrmse_std'][1],r['rmse'][0]))
    import numpy as np; a=np.array(accs).mean(0)
    print(f"[{tag:22s}] mean={a[0]:.4f} cd={a[1]:.4f} cl={a[2]:.4f} cd_rmse={a[3]:.5f}",flush=True)
run("baseline", epochs=70)
run("aug[35,90]exp-0.2", epochs=70, aug_vmin=35, aug_vmax=90)
run("aug[35,90]exp-0.18", epochs=70, aug_vmin=35, aug_vmax=90, aug_p=-0.18)
print("DONE",flush=True)
EOF
nohup python /tmp/exp3.py > /tmp/exp3.out 2>&1 &
echo "started $!"

output

started 547

bash

sleep 280 && cat /tmp/exp3.out

output

train n=550 (V<69.0), test n=80 (V in 69.0-74.9)
[baseline              ] mean=0.1220 cd=0.1435 cl=0.1005 cd_rmse=0.00060
[aug[35,90]exp-0.2     ] mean=0.0913 cd=0.1416 cl=0.0411 cd_rmse=0.00059

Now on the faithful split (test truly beyond train max): - baseline: mean 0.122, cd 0.144, cl 0.101 - aug[35,90] exp-0.2: mean 0.091, cd 0.142, cl 0.041 Augmentation now: - C_D: 0.1435 → 0.1416 (slightly BETTER, or at least not worse!) - C_L: 0.1005 → 0.0411 (much better) - mean: 0.122 → 0.091 (25% improvement) So on the FAITHFUL extrapolation split, augmentation helps both (C_D neutral-to-better, C_L much better). This confirms: the earlier "C_D worse" was an artifact of the within-range pseudo-test. The third run (exp-0.18) didn't finish in time. Let me wait for it. Actually DONE wasn't printed, so it's still running exp-0.18. Let me check again shortly. This is strong validation of my approach. Let me now finalize the strategy: 1. Velocity augmentation to densify/extend the velocity range, with C_D power law exp≈-0.2 and C_L invariant. 2. Train on all data. Now let me refine further. Remaining questions: - Best exponent (-0.18 vs -0.2 vs -0.22). - Augmentation range (vmax 85/90/95; vmin). - Augmentation ratio (how many augmented copies per original). - Geometry jitter. - Loss weighting (w_cd vs w_cl). - SWA for stability. - Epochs / LR schedule. - Whether to include val data in final training. Let me wait for exp3 to finish then run more tuning. Let me check the exp3 output again.

On the **faithful** extrapolation split (test truly beyond train max, mimicking reality), augmentation improves the mean NRMSE by 25% (0.122→0.091): C_L drops from 0.101 to 0.041 and C_D is neutral-to-slightly-better. This validates the core approach. Let me check the third config and run a tuning sweep over the exponent, range, and loss weighting.

bash

sleep 30; cat /tmp/exp3.out | tail -2

output

[baseline              ] mean=0.1220 cd=0.1435 cl=0.1005 cd_rmse=0.00060
[aug[35,90]exp-0.2     ] mean=0.0913 cd=0.1416 cl=0.0411 cd_rmse=0.00059

The exp-0.18 run is still going (2 reps * 70 epochs). Let me wait a bit more. Actually, let me not waste time; let me kill and run a more comprehensive tuning sweep in the background with a proper design. I'll use the faithful split (V<69 / V>=69) plus also evaluate on the real val set (V in normal range) to make sure I don't hurt in-range performance. Actually, a cleaner validation: I should evaluate the final approach on the REAL val set too (in-range) to ensure augmentation doesn't hurt in-distribution. But the scored set is out-of-range (high V). The faithful split is my best proxy for the scored set. Let me weight decisions primarily on the faithful split, but monitor real-val to avoid regressions. Let me design a comprehensive but time-boxed sweep. Each rep ~30-40s (70 epochs on 550 cases, augmented doubles to 1100). Let me do single rep for speed in the sweep, then confirm best with multiple reps. Actually variance matters. Let me use 2 reps but fewer configs per batch to stay within 300s, running in background. Let me check first how long a single 70-epoch augmented run takes: exp3 did baseline(2 reps, ~24s each=48s) + aug(2 reps ~60s each=120s) + partial. ~280s for ~4.5 runs. So augmented run ≈ 50-60s each. Let me batch sweeps of ~4 runs each in background, polling. Let me test exponents and ranges first. Let me run a sweep: exponents {-0.15,-0.2,-0.25} and ranges, plus jitter, plus loss weighting. I'll split into two background batches. Batch A (exponent + range): - aug[35,90] exp-0.15 - aug[35,95] exp-0.2 - aug[40,85] exp-0.2 - aug[35,90] exp-0.25 Let me run these with 2 reps each? That's 8 runs ~ 8*55=440s, too long for one wait. Let me do 1 rep each in batch (4 runs ~220s) and rely on confirming later. But 1-rep variance is high (~0.005-0.01 on mean). Hmm. Alternative: reduce epochs to 60 and do 2 reps. Let me just run in background and poll longer (I can sleep up to ~290s per call, and call multiple times). Let me kick off a bigger sweep in background and poll across multiple tool calls.

The core approach is validated. Now I'll run a broader tuning sweep in the background (exponent, augmentation range, jitter, loss weighting), evaluating on the faithful extrapolation split. I'll also track the in-range `val` set to avoid regressions. Let me launch it and poll across calls.

bash

cd /app && cat > /tmp/sweep.py << 'EOF'
import sys; sys.path.insert(0,"/app")
import torch, numpy as np
import lib
cfg = lib.load_cfg()
pts, tg = lib.load_split("train")
vpts, vtg = lib.load_split("val")
vel = pts[:,0,2]; thr=69.0
tp,tt = pts[vel<thr], tg[vel<thr]
ep,et = pts[vel>=thr], tg[vel>=thr]
def run(tag, reps=2, **kw):
    E=[]; V=[]
    for s in range(reps):
        model, norms = lib.train_model(tp, tt, cfg, seed=s, **kw)
        E.append(list(lib.nrmse_report(lib.predict(model, ep, *norms), et).values())[3:5])  # placeholder
        r = lib.nrmse_report(lib.predict(model, ep, *norms), et)
        rv = lib.nrmse_report(lib.predict(model, vpts, *norms), vtg)
        E.append((r['mean_nrmse_std'],r['nrmse_std'][0],r['nrmse_std'][1]))
        V.append(rv['mean_nrmse_std'])
    E=np.array([e for e in E if isinstance(e,tuple)]).mean(0); V=np.mean(V)
    print(f"[{tag:24s}] EXTRAP mean={E[0]:.4f} cd={E[1]:.4f} cl={E[2]:.4f} | inrange_val={V:.4f}",flush=True)
for tag,kw in [
  ("aug[35,90]exp-0.15",dict(aug_vmin=35,aug_vmax=90,aug_p=-0.15)),
  ("aug[35,90]exp-0.25",dict(aug_vmin=35,aug_vmax=90,aug_p=-0.25)),
  ("aug[35,95]exp-0.2", dict(aug_vmin=35,aug_vmax=95,aug_p=-0.20)),
  ("aug[45,85]exp-0.2", dict(aug_vmin=45,aug_vmax=85,aug_p=-0.20)),
]:
    run(tag, epochs=70, **kw)
print("DONE",flush=True)
EOF
nohup python /tmp/sweep.py > /tmp/sweep.out 2>&1 &
echo "started $!"

output

started 1650

bash

sleep 290 && cat /tmp/sweep.out

output

[aug[35,90]exp-0.15      ] EXTRAP mean=0.1050 cd=0.1682 cl=0.0418 | inrange_val=0.1328

Only one finished in 290s (the others running). exp-0.15 gives cd=0.168 (worse than exp-0.2 which was 0.142). So exp-0.15 is too mild. Let me wait for the rest. Let me note: in-range val mean = 0.1328 for this config. I should compare in-range val across configs too. Let me wait more.

exp−0.15 underperforms on C_D (0.168 vs 0.142 for exp−0.2), confirming a steeper exponent is needed. Let me wait for the remaining configs.

bash

sleep 200 && cat /tmp/sweep.out

output

[aug[35,90]exp-0.15      ] EXTRAP mean=0.1050 cd=0.1682 cl=0.0418 | inrange_val=0.1328
[aug[35,90]exp-0.25      ] EXTRAP mean=0.0903 cd=0.1405 cl=0.0401 | inrange_val=0.1230
[aug[35,95]exp-0.2       ] EXTRAP mean=0.1020 cd=0.1638 cl=0.0402 | inrange_val=0.1308

Now: - exp-0.15: cd 0.168, mean 0.105 - exp-0.25: cd 0.140, mean 0.090 (best so far) - exp-0.2 range[35,95]: cd 0.164, mean 0.102 - exp-0.2 range[35,90] (from exp3): cd 0.142, mean 0.091 Hmm interesting. exp-0.25 (0.090) ≈ exp-0.2 range[35,90] (0.091). But exp-0.2 range[35,95] got worse cd (0.164)? That's odd , wider range shouldn't hurt much. Could be seed variance (only 2 reps). Wait, the difference between [35,90] and [35,95] for exp-0.2: cd 0.142 vs 0.164. That's a notable difference for just extending vmax by 5. Might be variance. The in-range val: 0.123 (exp-0.25), 0.131 (exp-0.2 wide), 0.133 (exp-0.15). exp-0.25 looks best on both extrap (0.090) and in-range (0.123). But recall the empirical exponent from data was -0.21. Using -0.25 slightly over-corrects. But the faithful split test is V in [69,75]; the true local exponent there might be steeper. Earlier, fitting on V<68 gave exponent -0.228. So -0.25 isn't unreasonable. There's enough noise that I should be careful. Let me wait for the last run [45,85] and then do a focused comparison of the top candidates with MORE reps (3-4) to reduce variance, specifically: exp-0.2 vs exp-0.22 vs exp-0.25, all range [35,90]. Let me check the 4th result.

bash

sleep 60 && cat /tmp/sweep.out

output

[aug[35,90]exp-0.15      ] EXTRAP mean=0.1050 cd=0.1682 cl=0.0418 | inrange_val=0.1328
[aug[35,90]exp-0.25      ] EXTRAP mean=0.0903 cd=0.1405 cl=0.0401 | inrange_val=0.1230
[aug[35,95]exp-0.2       ] EXTRAP mean=0.1020 cd=0.1638 cl=0.0402 | inrange_val=0.1308

bash

sleep 45 && cat /tmp/sweep.out | tail -3

output

[aug[35,95]exp-0.2       ] EXTRAP mean=0.1020 cd=0.1638 cl=0.0402 | inrange_val=0.1308
[aug[45,85]exp-0.2       ] EXTRAP mean=0.0911 cd=0.1403 cl=0.0418 | inrange_val=0.1298
DONE

Summary of the sweep (faithful extrapolation split V<69/V>=69, 2 reps): | config | extrap mean | cd | cl | in-range val | |---|---|---|---|---| | baseline (exp3) | 0.122 | 0.144 | 0.101 | - | | exp-0.2 [35,90] (exp3) | 0.091 | 0.142 | 0.041 | - | | exp-0.15 [35,90] | 0.105 | 0.168 | 0.042 | 0.133 | | exp-0.25 [35,90] | 0.090 | 0.140 | 0.040 | 0.123 | | exp-0.2 [35,95] | 0.102 | 0.164 | 0.040 | 0.131 | | exp-0.2 [45,85] | 0.091 | 0.140 | 0.042 | 0.130 | Observations: - Range [35,90] and [45,85] with exp-0.2/-0.25 give the best and most consistent results (~0.090-0.091). - Range [35,95] was a bit worse on cd (0.164), possibly variance. - exp between -0.2 and -0.25 both good. - C_L is consistently ~0.040 regardless , augmentation solidly fixes it. - C_D is the remaining variable: best ~0.140. The C_D extrap nrmse (0.140) vs baseline (0.144) , augmentation helps C_D only marginally on THIS split because V∈[69,75] is close to train max 69 (only ~9% beyond). The big C_D extrapolation benefit shows at higher V (from the probe, baseline turns up badly at V>80). The real test is V 75-81, so augmentation's C_D benefit will be larger there than this split shows. Now, there's notable variance (±0.005-0.01). Let me reduce it by: 1. More reps in final decisions. 2. SWA (weight averaging) for stability. 3. Possibly a small ensemble via SWA. Let me now test additional levers on top of the good config (exp-0.22, range[35,90]): - SWA (swa_start around epoch 40-50 with more epochs, e.g. 90 epochs, swa_start=50). - Geometry jitter (0.01, 0.02 in standardized space). - Loss weighting (w_cd higher, since cd is the harder/worse target: cd 0.14 vs cl 0.04). - More epochs. - Huber loss (robust to outliers; C_D has some high outliers). Loss weighting insight: C_D nrmse (0.14) >> C_L nrmse (0.04). The metric is mean of the two (equal weight). To reduce the mean, improving C_D matters most now. But C_D is physically harder. Upweighting C_D in the loss might help it at the expense of C_L, but since C_L is already very low (0.04), I have room to trade. However, mean = (cd+cl)/2; if I push cd down by 0.01 and cl up by 0.01, mean unchanged. Only helps if upweighting improves cd MORE than it hurts cl. Let me test. Actually, a key consideration: the primary metric is mean_nrmse but there's also worst_nrmse (max of the two) as secondary. With cd=0.14, cl=0.04, worst=cd=0.14. Reducing cd reduces both mean and worst. So focusing on cd is doubly good, as long as cl stays well below cd. Let me also reconsider the target normalization for C_D. Since C_D is skewed and the metric might be NRMSE-by-std, MSE in std space is aligned. But C_D errors are dominated by high-C_D (high-aoa) cases. Let me check where C_D error concentrates. Actually, let me also consider predicting with a relative/log-aware loss for C_D. But output is affine... I train net to output standardized C_D. I could use a loss that weights by 1/C_D (relative error) to focus on getting small-C_D cases right proportionally. But the metric is absolute RMSE-based (nrmse by std), so absolute errors matter, meaning high-C_D cases dominate. Let me keep std-MSE but maybe Huber to be robust. Let me run a focused experiment on: SWA, jitter, loss weighting, huber, more epochs. Batch in background. Let me be strategic about time. Let me run a batch of ~5 configs, 2 reps each, ~50s each = 500s. I'll poll twice. Configs (all range[35,90], exp-0.22 as a middle ground): 1. base: exp-0.22, 70ep (reference) 2. +SWA: 100ep, swa_start=60 3. +jitter 0.02 4. +w_cd=2.0 5. huber delta=1.0, 70ep Let me run. Actually, let me reconsider the exponent choice once more. The data fit gave -0.21 overall, -0.228 for V<68. exp-0.25 did marginally best but within noise. I'll go with -0.22 as a robust central value, and maybe test -0.22 vs -0.25 more carefully with the other improvements. Let me also reconsider augmentation amount. Currently I add exactly 1 augmented copy per original (doubling). I could add 2 copies (tripli …[truncated]

Clear synthesis: range **[35,90]** with exponent **−0.2 to −0.25** is the sweet spot (extrap mean ≈0.090, C_L solidly ≈0.040, best in-range val 0.123). C_L is fully fixed by augmentation; C_D (≈0.14) is now the bottleneck and also the `worst_nrmse`, so improving it helps all metrics. Let me add multi-copy augmentation support and test stabilizers (SWA, jitter, loss weighting, Huber) on top.

str_replace_based_edit_tool

command: str_replace
path: /app/lib.py
old_str: def velocity_augment(pts, tg, vmin, vmax, p=CD_VEL_EXPONENT, gen=None):
    """Rescale each case's inlet velocity to a random target in [vmin,vmax];
    adjust C_D by the power law V^p and keep C_L unchanged."""
    gen = gen or torch.Generator().manual_seed(0)
    n = pts.shape[0]
    v_old = pts[:, 0, V_I].clone()
    v_new = vmin + (vmax - vmin) * torch.rand(n, generator=gen)
    ratio = v_new / v_old
    pts2 = pts.clone()
    pts2[:, :, V_I] = v_new.view(-1, 1)
    tg2 = tg.clone()
    tg2[:, CD_I] = tg[:, CD_I] * ratio.pow(p)
    return pts2, tg2
new_str: def velocity_augment(pts, tg, vmin, vmax, p=CD_VEL_EXPONENT, gen=None, n_aug=1):
    """Rescale each case's inlet velocity to a random target in [vmin,vmax];
    adjust C_D by the power law V^p and keep C_L unchanged. Returns n_aug copies."""
    gen = gen or torch.Generator().manual_seed(0)
    outs_p, outs_t = [], []
    n = pts.shape[0]
    v_old = pts[:, 0, V_I]
    for _ in range(n_aug):
        v_new = vmin + (vmax - vmin) * torch.rand(n, generator=gen)
        ratio = v_new / v_old
        pts2 = pts.clone()
        pts2[:, :, V_I] = v_new.view(-1, 1)
        tg2 = tg.clone()
        tg2[:, CD_I] = tg[:, CD_I] * ratio.pow(p)
        outs_p.append(pts2)
        outs_t.append(tg2)
    return torch.cat(outs_p, 0), torch.cat(outs_t, 0)

output

The file /app/lib.py has been edited successfully.

bash

cd /app && sed -i 's/aug_vmax=None, aug_vmin=None, aug_p=CD_VEL_EXPONENT,/aug_vmax=None, aug_vmin=None, aug_p=CD_VEL_EXPONENT, n_aug=1,/' lib.py
# update the two velocity_augment calls inside train_model/_update_bn to pass n_aug, and pass n_aug through _update_bn
python - << 'EOF'
import re
s=open('/app/lib.py').read()
s=s.replace("ap, at = velocity_augment(train_pts, train_tg, aug_vmin, aug_vmax, aug_p, gen)\n            pool_pts",
            "ap, at = velocity_augment(train_pts, train_tg, aug_vmin, aug_vmax, aug_p, gen, n_aug)\n            pool_pts")
s=s.replace("ap, at = velocity_augment(train_pts, train_tg, aug_vmin, aug_vmax, aug_p, gen)\n            ep_pts",
            "ap, at = velocity_augment(train_pts, train_tg, aug_vmin, aug_vmax, aug_p, gen, n_aug)\n            ep_pts")
s=s.replace("_update_bn(model, train_pts, train_tg, fmean, fstd, tmean, tstd, aug_vmin, aug_vmax, aug_p, gen, bs)",
            "_update_bn(model, train_pts, train_tg, fmean, fstd, tmean, tstd, aug_vmin, aug_vmax, aug_p, gen, bs, n_aug)")
s=s.replace("def _update_bn(model, train_pts, train_tg, fmean, fstd, tmean, tstd, aug_vmin, aug_vmax, aug_p, gen, bs):",
            "def _update_bn(model, train_pts, train_tg, fmean, fstd, tmean, tstd, aug_vmin, aug_vmax, aug_p, gen, bs, n_aug=1):")
s=s.replace("ap, at = velocity_augment(train_pts, train_tg, aug_vmin, aug_vmax, aug_p, gen)\n        pts = torch.cat",
            "ap, at = velocity_augment(train_pts, train_tg, aug_vmin, aug_vmax, aug_p, gen, n_aug)\n        pts = torch.cat")
open('/app/lib.py','w').write(s)
print("patched")
EOF
python -c "import sys; sys.path.insert(0,'/app'); import lib; print('import OK')"
grep -n "n_aug" lib.py

output

patched
import OK
35:def velocity_augment(pts, tg, vmin, vmax, p=CD_VEL_EXPONENT, gen=None, n_aug=1):
37:    adjust C_D by the power law V^p and keep C_L unchanged. Returns n_aug copies."""
42:    for _ in range(n_aug):
100:    aug_vmax=None, aug_vmin=None, aug_p=CD_VEL_EXPONENT, n_aug=1,
114:            ap, at = velocity_augment(train_pts, train_tg, aug_vmin, aug_vmax, aug_p, gen, n_aug)
133:            ap, at = velocity_augment(train_pts, train_tg, aug_vmin, aug_vmax, aug_p, gen, n_aug)
175:        _update_bn(model, train_pts, train_tg, fmean, fstd, tmean, tstd, aug_vmin, aug_vmax, aug_p, gen, bs, n_aug)
179:def _update_bn(model, train_pts, train_tg, fmean, fstd, tmean, tstd, aug_vmin, aug_vmax, aug_p, gen, bs, n_aug):
187:        ap, at = velocity_augment(train_pts, train_tg, aug_vmin, aug_vmax, aug_p, gen, n_aug)

Now let me run the stabilizer/enhancer sweep. Let me verify SWA works first with a quick smoke test, then the batch. Let me run the batch of enhancers on the faithful split.

Now let me test the stabilizers and enhancers on top of the good config. I'll run them in the background with 2 reps each.

bash

cd /app && cat > /tmp/sweep2.py << 'EOF'
import sys; sys.path.insert(0,"/app")
import numpy as np
import lib
cfg = lib.load_cfg()
pts, tg = lib.load_split("train")
vpts, vtg = lib.load_split("val")
vel = pts[:,0,2]; thr=69.0
tp,tt = pts[vel<thr], tg[vel<thr]
ep,et = pts[vel>=thr], tg[vel>=thr]
def run(tag, reps=3, **kw):
    E=[];V=[]
    for s in range(reps):
        model, norms = lib.train_model(tp, tt, cfg, seed=s, **kw)
        r = lib.nrmse_report(lib.predict(model, ep, *norms), et)
        rv = lib.nrmse_report(lib.predict(model, vpts, *norms), vtg)
        E.append((r['mean_nrmse_std'],r['nrmse_std'][0],r['nrmse_std'][1]))
        V.append(rv['mean_nrmse_std'])
    E=np.array(E).mean(0); V=np.mean(V)
    print(f"[{tag:22s}] EXTRAP mean={E[0]:.4f} cd={E[1]:.4f} cl={E[2]:.4f} | inrange={V:.4f}",flush=True)
base=dict(aug_vmin=35,aug_vmax=90,aug_p=-0.22)
run("ref exp-0.22 70ep", epochs=70, **base)
run("SWA 110ep start60", epochs=110, swa_start=60, **base)
run("jitter0.02 70ep", epochs=70, jitter=0.02, **base)
run("w_cd2 70ep", epochs=70, w_cd=2.0, **base)
run("n_aug2 70ep", epochs=70, n_aug=2, **base)
print("DONE",flush=True)
EOF
nohup python /tmp/sweep2.py > /tmp/sweep2.out 2>&1 &
echo "started $!"

output

started 4070

bash

sleep 290 && cat /tmp/sweep2.out

output

[ref exp-0.22 70ep     ] EXTRAP mean=0.1022 cd=0.1649 cl=0.0395 | inrange=0.1278

Hmm, with 3 reps, the reference exp-0.22 gives cd=0.165, mean=0.102. Earlier with 2 reps exp-0.2 gave cd=0.142, exp-0.25 gave cd=0.140. Now exp-0.22 gives cd=0.165? That's higher. There's substantial variance between runs/seeds. The cd extrap metric is noisy (±0.02). This makes tuning the exponent by this split unreliable. I need a more robust evaluation. The issue: the faithful split test set is only 80 cases, and C_D extrap nrmse is noisy across seeds. Let me: 1. Use more reps and/or average predictions across seeds (ensemble) to reduce noise in the EVALUATION of a config. 2. Consider a more stable metric. Actually, the seed variance in the trained model is real and will affect my final single checkpoint too. So SWA and/or ensembling-into-one (weight averaging across seeds won't work well for different random inits , weight averaging only works along a single trajectory or with careful setup). Hmm, but I can only submit ONE model (one state_dict). So I can't ensemble multiple independently-initialized networks at inference. I CAN use SWA (average along one training trajectory) which stays in one basin. Given the variance, let me focus on robust choices: - SWA should reduce variance and improve generalization. Let me see its result. - The exponent: I'll rely on the physics (-0.2 to -0.22) rather than noisy tuning. Let me wait for the rest of sweep2. The key comparisons: does SWA help? does jitter help? w_cd? n_aug? Let me wait more.

There's meaningful seed-to-seed variance in the C_D extrapolation metric (±0.02), so I shouldn't over-tune the exponent on it. Let me wait for the stabilizers , SWA in particular should reduce variance, which matters since I can only submit one checkpoint.

bash

sleep 280 && cat /tmp/sweep2.out

output

[ref exp-0.22 70ep     ] EXTRAP mean=0.1022 cd=0.1649 cl=0.0395 | inrange=0.1278
[SWA 110ep start60     ] EXTRAP mean=0.1005 cd=0.1669 cl=0.0342 | inrange=0.1074

SWA: - EXTRAP mean 0.1005 (vs ref 0.1022) , slightly better - cd 0.167 (similar to ref 0.165) - cl 0.034 (better than 0.040) - in-range 0.107 (much better than 0.128!) So SWA improves in-range val a lot (0.128→0.107) and slightly improves extrap. Good , SWA helps generalization and stability. I'll use SWA. Let me wait for jitter, w_cd, n_aug. Note cd extrap ~0.165-0.167 in these runs, higher than the 0.14 seen earlier , so the earlier 0.14 was a lucky 2-rep draw. The honest cd extrap on this split is ~0.15-0.16. The variance is frustrating. Let me think about whether I can reduce C_D error more fundamentally. The C_D extrap error of ~0.16 nrmse corresponds to rmse ~0.00066 (std of et cd ~0.004). Actually nrmse_std 0.16 * std. Let me compute: et cd std? The test cd std , let me not worry, it's relative. What else could improve C_D? 1. Better feature handling. The network must extract aoa and geometry. aoa is a per-point constant channel. Fine. 2. The C_D depends strongly on aoa² (high aoa → high drag). Extreme aoa cases have high C_D and dominate absolute error. Maybe the network underfits the high-C_D tail. Huber or weighting could help, but Huber reduces outlier influence (opposite). Actually to fit the high-C_D tail better, I might want to NOT downweight them. Standard MSE already emphasizes large errors. 3. More epochs / better optimization. Actually, let me reconsider the whole C_D modeling. The metric is nrmse (RMSE/std). For C_D, the dynamic range is dominated by aoa. The network gets aoa directly. So it should nail the aoa trend. The residual is shape-dependent (thickness etc.) + velocity. Let me check: what's the best POSSIBLE cd nrmse on this split using the physical model (exp in aoa poly + V^-0.2)? That sets a floor. Earlier log-CD model R²=0.82 → but that's log space. Let me compute the physical-model cd nrmse on the faithful split to know what's achievable, and compare to the network's 0.16. Let me also reconsider: maybe the network can beat the simple physical model because it uses full geometry. The residual after aoa+V had corr 0.49 with thickness , so geometry explains a good chunk of the residual. A PointNet using full geometry should beat the aoa-only physical model. Let me compute the physical model baseline on the faithful split while waiting. Let me do it in a separate quick command. Actually the sweep2 is using CPU heavily (background). Let me wait for it to finish to avoid contention, then analyze. Let me wait for the remaining 3 configs.

SWA clearly helps: in-range val improves 0.128→0.107 and extrap is slightly better with lower C_L (0.034), so I'll adopt it. The honest C_D extrap is ≈0.16 (earlier 0.14 was a lucky draw). Let me wait for the remaining configs, then quantify the achievable C_D floor with a pure-physics model for context.

bash

sleep 200 && cat /tmp/sweep2.out

output

[ref exp-0.22 70ep     ] EXTRAP mean=0.1022 cd=0.1649 cl=0.0395 | inrange=0.1278
[SWA 110ep start60     ] EXTRAP mean=0.1005 cd=0.1669 cl=0.0342 | inrange=0.1074
[jitter0.02 70ep       ] EXTRAP mean=0.1362 cd=0.2309 cl=0.0414 | inrange=0.1519

Jitter 0.02 HURTS C_D badly (0.165→0.231). Makes sense , jittering geometry destroys the subtle shape→drag signal. So NO geometry jitter, or much smaller. I'll drop jitter. Let me wait for w_cd and n_aug.

Jitter hurts C_D badly (0.165→0.231) , it destroys the subtle shape→drag signal, so I'll drop it. Let me get the last two configs.

bash

sleep 120 && cat /tmp/sweep2.out | tail -3

output

[SWA 110ep start60     ] EXTRAP mean=0.1005 cd=0.1669 cl=0.0342 | inrange=0.1074
[jitter0.02 70ep       ] EXTRAP mean=0.1362 cd=0.2309 cl=0.0414 | inrange=0.1519
[w_cd2 70ep            ] EXTRAP mean=0.1028 cd=0.1623 cl=0.0433 | inrange=0.1238

bash

sleep 60 && cat /tmp/sweep2.out | tail -2

output

[jitter0.02 70ep       ] EXTRAP mean=0.1362 cd=0.2309 cl=0.0414 | inrange=0.1519
[w_cd2 70ep            ] EXTRAP mean=0.1028 cd=0.1623 cl=0.0433 | inrange=0.1238

n_aug2 still running. w_cd2: cd 0.162 (marginally better than 0.165), cl 0.043 (worse). Net mean ~same. Not clearly helpful. I'll keep w_cd modest, maybe 1.3-1.5, or just 1.0. Given the metric weights both equally and worst_nrmse is cd, slight cd emphasis is fine but w_cd=2 traded too much cl. Let me keep w_cd around 1.0-1.5. Let me wait for n_aug2. While waiting, let me compute the physics-model C_D floor on the faithful split, to understand how much room there is. Let me run it quickly (it's lightweight, won't contend much). Actually, let me wait for n_aug result first to not contend.

While `n_aug2` finishes, let me quantify the achievable C_D floor: fit a pure-physics model (polynomial in aoa × V^p) on the low-velocity cases and evaluate its C_D NRMSE on the faithful extrapolation split, for context on how much headroom the network has.

bash

sleep 45; cat /tmp/sweep2.out | tail -1; echo "---physics floor---"; cd /app && python -c "
import sys; sys.path.insert(0,'/app'); import torch, numpy as np, lib
pts,tg=lib.load_split('train'); vel=pts[:,0,2].numpy(); aoa=pts[:,0,3].numpy(); cd=tg[:,0].numpy()
thr=69.0; tr=vel<thr; te=vel>=thr
lv=np.log(vel); lcd=np.log(cd)
# physics: log cd = poly(aoa deg2) + p*log v, fit on train
X=lambda m: np.stack([np.ones(m.sum()),aoa[m],aoa[m]**2,lv[m]],1)
b,*_=np.linalg.lstsq(X(tr),lcd[tr],rcond=None)
pred=np.exp(X(te)@b)
rmse=np.sqrt(((pred-cd[te])**2).mean()); nrmse=rmse/cd[te].std()
print(f'physics aoa2+V^{b[-1]:.3f}: cd extrap nrmse={nrmse:.4f} rmse={rmse:.5f}')
# add cubic aoa
X3=lambda m: np.stack([np.ones(m.sum()),aoa[m],aoa[m]**2,aoa[m]**3,lv[m]],1)
b3,*_=np.linalg.lstsq(X3(tr),lcd[tr],rcond=None); pred3=np.exp(X3(te)@b3)
print(f'physics aoa3+V: cd extrap nrmse={np.sqrt(((pred3-cd[te])**2).mean())/cd[te].std():.4f}')
print(f'test cd std={cd[te].std():.5f} mean={cd[te].mean():.5f}')
"

output

[w_cd2 70ep            ] EXTRAP mean=0.1028 cd=0.1623 cl=0.0433 | inrange=0.1238
---physics floor---
physics aoa2+V^-0.227: cd extrap nrmse=0.4049 rmse=0.00168
physics aoa3+V: cd extrap nrmse=0.4069
test cd std=0.00415 mean=0.01180

The pure-physics model (aoa polynomial + V^-0.227) gives C_D extrap nrmse = 0.40, much WORSE than the PointNet's 0.16! So the PointNet's use of full geometry is hugely valuable for C_D , it captures shape-dependent drag (thickness, camber) that the aoa-only model misses. The network at 0.16 is way better than physics-only at 0.40. Good , this means the network is doing real work, and there isn't a trivial better model. The n_aug2 run didn't print yet (still the w_cd2 as last line). Let me check once more. So my conclusions so far: - Velocity augmentation [35,90], exp ~-0.2 to -0.22: essential, fixes C_L, keeps C_D sane at extrapolation. - SWA: helps stability & in-range, slight extrap help. USE IT. - Jitter: harmful. SKIP. - w_cd=2: not helpful. Keep ~1.0-1.3. - n_aug: pending. Let me check n_aug2 result.

The pure-physics C_D model scores only 0.40 NRMSE vs. the PointNet's 0.16 , the network's use of full geometry is doing substantial work on shape-dependent drag, and there's no trivially better model. Let me confirm the `n_aug` result.

bash

sleep 30; tail -2 /tmp/sweep2.out

output

[jitter0.02 70ep       ] EXTRAP mean=0.1362 cd=0.2309 cl=0.0414 | inrange=0.1519
[w_cd2 70ep            ] EXTRAP mean=0.1028 cd=0.1623 cl=0.0433 | inrange=0.1238

bash

sleep 40; tail -1 /tmp/sweep2.out; ps aux | grep -c "[s]weep2"

output

[w_cd2 70ep            ] EXTRAP mean=0.1028 cd=0.1623 cl=0.0433 | inrange=0.1238
1

bash

sleep 45; tail -2 /tmp/sweep2.out

output

[jitter0.02 70ep       ] EXTRAP mean=0.1362 cd=0.2309 cl=0.0414 | inrange=0.1519
[w_cd2 70ep            ] EXTRAP mean=0.1028 cd=0.1623 cl=0.0433 | inrange=0.1238

n_aug2 is taking long (3 reps * 70 epochs * tripled data). Let me wait a bit more. Meanwhile, let me plan the final design and the robust validation. Given variance, my final decisions: - Augmentation: range [35, 92], exp -0.21 (physics central value), n_aug=1 or 2 (pending). - SWA with ~120 epochs, swa_start ~70. - No jitter. - w_cd ~1.0 (or slight 1.2). - Loss: MSE in standardized space. Now, a crucial decision for the FINAL model: I'll train on ALL 630 training cases (V up to 75) plus augmentation up to ~92. Should I also include the val set (80 cases) in training? The val set is in-range (V 31-74), so it adds in-range data. Including it gives more training data, which helps. But I lose my in-range validation signal. Since I've already done model selection, I could fold val into training for the final model. But the scored set is out-of-range, so in-range val data is less directly relevant , still, more airfoil shapes = better geometry generalization, which helps C_D/C_L at all velocities. I think including val (+ its augmentation) is net positive. I'll train final on train+val. But I want a final sanity check of the exact final config on the faithful extrapolation split (and maybe a second split) before committing. Let me also build a more robust extrapolation validator: average over multiple cutoffs / seeds. Let me reconsider whether to make the final prediction more robust by using the fresh-augmentation SWA (already does). Let me wait for n_aug2, then run a final validation of the chosen config with several seeds and also a second, more extreme split (train V<63, test V>=70 with a gap) to double check C_D extrapolation at larger steps. Let me check n_aug2.

bash

sleep 30; tail -1 /tmp/sweep2.out

output

DONE

bash

grep n_aug2 /tmp/sweep2.out

output

[n_aug2 70ep           ] EXTRAP mean=0.1453 cd=0.2502 cl=0.0405 | inrange=0.1361

n_aug2 HURT C_D badly (0.165→0.250)! Interesting. Adding 2 augmented copies per original (tripling, so 2/3 augmented, 1/3 original) over-emphasizes the velocity-scaled synthetic data and dilutes the real C_D signal, hurting C_D. So n_aug=1 (50/50) is better. I'll keep n_aug=1. Wait, but with fresh augmentation each epoch, even n_aug=1 means over epochs the net sees many velocity variants. The ratio of original:augmented within each batch matters for the gradient balance. n_aug=1 → 50/50. More augmented → C_D degrades because the power-law-scaled labels have some noise/bias and dominate. Actually, I wonder if I should use LESS augmentation (e.g., augment only a fraction of cases, keeping more originals). But n_aug=1 already gives good C_L fix. Let me consider an option: keep all originals + augment, but the augmented C_D uses exact power law. The degradation with more augmentation suggests the power law isn't perfect per-case and adds label noise to C_D. With n_aug=1, the 50% real cases anchor C_D. So final: n_aug=1, i.e., 50/50 original/augmented. Hold on , reconsider. Could I reduce the C_D label noise from augmentation by using a better per-case exponent? The exponent varies with aoa (from the interaction model). But that's second-order. Keep -0.21. Alternatively: only augment to teach C_L invariance and C_D trend, but anchor C_D more. What if augmented cases only modify velocity for the C_L-invariance lesson, but I DON'T include augmented cases' C_D in the loss (mask C_D loss on augmented, only apply C_L loss on augmented)? That way: - Original cases: full loss on both C_D and C_L (anchors absolute C_D). - Augmented cases: loss ONLY on C_L (teaches C_L velocity-invariance) , and maybe also on C_D with the power law but downweighted. Hmm, but then the network wouldn't learn the C_D velocity TREND from augmentation, and baseline C_D extrapolates wrong (upturn). So I DO need augmented C_D labels to teach the trend. The power law is the best trend estimate I have. Let me try a refined approach: augmented cases contribute to C_D loss but at a REDUCED weight (so originals anchor absolute level, augmented teach the trend). This could get the best of both. Let me add a param `aug_cd_weight` that scales the C_D loss for augmented rows. Actually this requires tracking which rows are augmented in the loss. Let me implement a cleaner training variant. Actually, let me reconsider the simplest high-impact path. The current best config (SWA, aug[35,90] exp-0.21, n_aug=1, no jitter) gives extrap mean ~0.10, cd~0.16, cl~0.034. That's already a big improvement over baseline (0.122, cd 0.144, cl 0.101)... wait, baseline cd was 0.144 and augmented cd is 0.16?? On THIS faithful split (V in 69-75), baseline C_D (0.144) is actually BETTER than augmented (0.16)! Because this split's test is only ~9% beyond train max, where baseline's upturn hasn't kicked in much, and augmentation adds label noise to C_D. The augmentation's C_D benefit only appears at LARGER extrapolation (V>80), as the probe showed. So on this particular faithful split, augmentation trades a bit of C_D (0.144→0.16, +0.016) for a lot of C_L (0.101→0.034, -0.067). Net mean improves. But I'm worried about C_D on the REAL test (V 75-81). The REAL test is V in [75, 81.6]. Train max is 75. So real test is 0-9% beyond train max , very similar to this faithful split (V 69-75, which is 0-9% beyond cutoff 69)! So this faithful split is actually a GOOD proxy for the real test. And on it, augmentation's C_D is slightly worse than baseline, but C_L much better. Hmm, so maybe for the real test, I want augmentation primarily for C_L, but I want to minimize its C_D damage. Let me reconsider: the probe showed at V=75-80 the baseline C_D turns UP (wrong) while augmented follows power law (right). But the faithful split (real cases at V 69-75) shows baseline C_D is fine there. Let me reconcile: the baseline upturn in the probe started around V=70-75 and became significant at V>80. At …[truncated]

`n_aug=2` hurts C_D (0.165→0.250): over-weighting the power-law-scaled synthetic labels dilutes the real C_D anchor. So 50/50 (n_aug=1) is best. Key realization: the faithful split (V 69–75, i.e. 0–9% beyond cutoff) is an excellent proxy for the **real** test (V 75–81.6, 0–9% beyond train max 75). On it, augmentation trades a little C_D for a large C_L gain. Let me try to recover that C_D by **downweighting C_D loss on augmented rows** (originals anchor the absolute level; augmented still teach the trend + C_L invariance). I'll implement per-row C_D weighting.

str_replace_based_edit_tool

command: str_replace
path: /app/lib.py
old_str:     aug_vmax=None, aug_vmin=None, aug_p=CD_VEL_EXPONENT, n_aug=1,
new_str:     aug_vmax=None, aug_vmin=None, aug_p=CD_VEL_EXPONENT, n_aug=1, aug_cd_weight=1.0,

output

The file /app/lib.py has been edited successfully.

str_replace_based_edit_tool

command: str_replace
path: /app/lib.py
old_str:         perm = torch.randperm(m, generator=gen)
        for s in range(0, m, bs):
            idx = perm[s:s+bs]
            xb, yb = xs[idx], ys[idx]
            opt.zero_grad(set_to_none=True)
            out = model(xb)
            if loss_type == "huber":
                per = F.huber_loss(out, yb, reduction="none", delta=huber_delta)
            else:
                per = (out - yb) ** 2
            loss = (per * wvec.view(1, -1)).mean()
new_str:         # per-row C_D weight: 1.0 for originals, aug_cd_weight for augmented rows
        cd_rw = torch.ones(m)
        if aug_vmax is not None and aug_cd_weight != 1.0:
            cd_rw[n:] = aug_cd_weight
        perm = torch.randperm(m, generator=gen)
        for s in range(0, m, bs):
            idx = perm[s:s+bs]
            xb, yb = xs[idx], ys[idx]
            opt.zero_grad(set_to_none=True)
            out = model(xb)
            if loss_type == "huber":
                per = F.huber_loss(out, yb, reduction="none", delta=huber_delta)
            else:
                per = (out - yb) ** 2
            rw = torch.ones_like(per)
            rw[:, CD_I] = cd_rw[idx]
            loss = (per * wvec.view(1, -1) * rw).mean()

output

The file /app/lib.py has been edited successfully.

Wait, there's a subtle bug: the augmentation generates [orig, aug] order, but I need to make sure ep_pts/ep_tg are in that order. Looking at the code: `ep_pts = torch.cat([train_pts, ap], 0)` , yes, originals first (rows 0..n-1), augmented after (n..m-1). Good. And `n = train_pts.shape[0]` is set before the loop. Good. But when aug_cd_weight != 1.0, the mean of the loss changes (weighted). That's fine for gradient direction. But note: using .mean() over a weighted tensor where some weights <1 reduces the effective loss magnitude slightly; LR interaction is minor. Fine. Let me verify import and run the comparison.

bash

cd /app && python -c "import sys; sys.path.insert(0,'/app'); import lib; print('OK')" && cat > /tmp/sweep3.py << 'EOF'
import sys; sys.path.insert(0,"/app")
import numpy as np, lib
cfg = lib.load_cfg()
pts, tg = lib.load_split("train"); vpts, vtg = lib.load_split("val")
vel = pts[:,0,2]; thr=69.0
tp,tt = pts[vel<thr], tg[vel<thr]; ep,et = pts[vel>=thr], tg[vel>=thr]
def run(tag, reps=3, **kw):
    E=[];V=[]
    for s in range(reps):
        model, norms = lib.train_model(tp, tt, cfg, seed=s, **kw)
        r = lib.nrmse_report(lib.predict(model, ep, *norms), et)
        rv = lib.nrmse_report(lib.predict(model, vpts, *norms), vtg)
        E.append((r['mean_nrmse_std'],r['nrmse_std'][0],r['nrmse_std'][1])); V.append(rv['mean_nrmse_std'])
    E=np.array(E).mean(0); V=np.mean(V)
    print(f"[{tag:20s}] EXTRAP mean={E[0]:.4f} cd={E[1]:.4f} cl={E[2]:.4f} | inrange={V:.4f}",flush=True)
base=dict(aug_vmin=35,aug_vmax=90,aug_p=-0.21,epochs=110,swa_start=65)
run("cd_aug_w1.0", **base)
run("cd_aug_w0.5", aug_cd_weight=0.5, **base)
run("cd_aug_w0.3", aug_cd_weight=0.3, **base)
print("DONE",flush=True)
EOF
nohup python /tmp/sweep3.py > /tmp/sweep3.out 2>&1 &
echo "started $!"

output

OK
started 8096

bash

sleep 295 && cat /tmp/sweep3.out

output

<system>Tool ran without output or errors</system>

Empty output after 295s , SWA with 110 epochs takes longer (~80-90s per rep * 3 reps = 270s for the first config). Let me wait more.

SWA runs at 110 epochs take longer. Let me wait for results.

bash

sleep 250 && cat /tmp/sweep3.out

output

[cd_aug_w1.0         ] EXTRAP mean=0.0990 cd=0.1643 cl=0.0337 | inrange=0.1090

bash

sleep 290 && cat /tmp/sweep3.out

output

[cd_aug_w1.0         ] EXTRAP mean=0.0990 cd=0.1643 cl=0.0337 | inrange=0.1090
[cd_aug_w0.5         ] EXTRAP mean=0.0923 cd=0.1503 cl=0.0344 | inrange=0.1026

cd_aug_w0.5 helps: - cd 0.164 → 0.150 (better!) - cl 0.034 (same) - mean 0.099 → 0.092 - in-range 0.109 → 0.103 So downweighting C_D loss on augmented rows to 0.5 helps C_D while keeping C_L benefit. Let me see w0.3.

Downweighting augmented-row C_D loss to 0.5 helps: C_D improves 0.164→0.150 while C_L stays 0.034 and in-range improves too. This confirms the hypothesis , originals anchor the absolute C_D level while augmented rows still teach the trend. Let me see the 0.3 result.

bash

sleep 200 && cat /tmp/sweep3.out

output

[cd_aug_w1.0         ] EXTRAP mean=0.0990 cd=0.1643 cl=0.0337 | inrange=0.1090
[cd_aug_w0.5         ] EXTRAP mean=0.0923 cd=0.1503 cl=0.0344 | inrange=0.1026
[cd_aug_w0.3         ] EXTRAP mean=0.0863 cd=0.1382 cl=0.0345 | inrange=0.1012
DONE

cd_aug_w0.3 is even better: - cd 0.138 (vs 0.150 at 0.5, 0.164 at 1.0) - cl 0.0345 (same) - mean 0.086 - in-range 0.101 So lower aug_cd_weight → better C_D. The trend suggests going even lower. But there's a limit: if aug_cd_weight→0, the augmented rows don't teach the C_D velocity trend at all, and C_D extrapolation would revert toward the baseline's wrong upturn at high V. On THIS faithful split (only 9% beyond), the baseline C_D (no trend teaching) was 0.144, which is similar to w0.3's 0.138. But at the REAL test (V up to 81, and the hidden set might go higher), the trend teaching matters more. So there's a tension: - Low aug_cd_weight → better absolute C_D anchoring (helps on mild extrapolation like this split), but weaker trend teaching (risks larger extrapolation). - High aug_cd_weight → strong trend teaching (robust to large extrapolation), but noisier absolute C_D. Since this faithful split only tests ~9% beyond, it FAVORS low aug_cd_weight. But the real/hidden test might extrapolate further. I should pick a value that balances both. w0.3-0.5 seems reasonable. But let me verify with a MORE EXTREME extrapolation split to see if very low aug_cd_weight breaks down at larger V steps. Let me test on a split with a bigger gap: train V<60, test V>=70 (test is 70-75, which is up to 75/60=1.25, 25% beyond cutoff 60). This tests larger extrapolation. Compare aug_cd_weight 0.3, 0.5, 1.0, and baseline. If w0.3 holds up, use it; if it degrades badly at larger steps, prefer 0.5. Actually, the real test max (81.6) / train max (75) = 1.088, only 9% beyond. The hidden scored set is "sampled from the higher-speed, higher-Reynolds end" , likely similar to public_test (75-81.6). So ~9% beyond is the realistic scenario, maybe up to 15% to be safe. The faithful split (9% beyond) is the right proxy. But to be safe against a slightly larger extrapolation, let me check the more extreme split. Let me also reconsider: the probe earlier showed that at V=85-90, even with full augmentation the C_D follows power law (good). With low aug_cd_weight, would C_D still follow the power law at V=85-90, or revert to upturn? Let me probe the learned velocity response for w0.3 vs w1.0 at high V. That's the key diagnostic. Let me run a probe comparing cd_aug_w 0.3, 0.5, 1.0 on the velocity sweep (trained on V<69) to see C_D behavior at V=75,80,85,90. This directly shows whether low weight preserves the trend in the real test regime (75-81) and beyond. Let me do that probe. It also lets me verify C_L stays flat. This is more informative than another split. Let me run it.

Lower `aug_cd_weight` keeps improving C_D on this mild (9%) extrapolation split. But there's a tradeoff: too low and the augmented rows stop teaching the velocity *trend*, which matters for larger extrapolation. Since the real test can extend ~9–15% beyond train max (and the hidden set could reach further), let me probe the actual learned C_D velocity response at high V for each weight to ensure the trend survives in the 75–90 regime.

bash

cd /app && cat > /tmp/probe2.py << 'EOF'
import sys; sys.path.insert(0,"/app")
import torch, numpy as np, lib
cfg = lib.load_cfg()
pts, tg = lib.load_split("train"); vel=pts[:,0,2]; thr=69.0
tp,tt = pts[vel<thr], tg[vel<thr]
sel = tp[:40].clone()
vs=[60,69,75,80,85,90]
def sweep(w):
    m,n = lib.train_model(tp,tt,cfg,seed=0,aug_vmin=35,aug_vmax=90,aug_p=-0.21,epochs=110,swa_start=65,aug_cd_weight=w)
    cds=[]
    for v in vs:
        s=sel.clone(); s[:,:,2]=v
        cds.append(lib.predict(m,s,*n)[:,0].median().item())
    base=cds[0]
    print(f"w={w}: CDr", " ".join(f"{c/base:.4f}" for c in cds))
print("V          :", " ".join(f"{v:6d}" for v in vs))
print("power V^-.21:", " ".join(f"{(v/60)**-0.21:.4f}" for v in vs))
for w in [1.0,0.5,0.3,0.15]:
    sweep(w)
print("DONE",flush=True)
EOF
nohup python /tmp/probe2.py > /tmp/probe2.out 2>&1 &
echo "started $!"

output

started 12126

bash

sleep 295 && cat /tmp/probe2.out

output

<system>Tool ran without output or errors</system>

bash

sleep 140 && cat /tmp/probe2.out

output

<system>Tool ran without output or errors</system>

Still nothing after ~435s. 4 SWA runs at 110 epochs * ~85s = 340s, plus the print at the end. Should be close. Let me wait a bit more.

bash

sleep 60 && cat /tmp/probe2.out

output

<system>Tool ran without output or errors</system>

bash

ps aux | grep "[p]robe2" >/dev/null && echo "still running"; sleep 60; cat /tmp/probe2.out

output

still running
V          :     60     69     75     80     85     90
power V^-.21: 1.0000 0.9711 0.9542 0.9414 0.9295 0.9184
w=1.0: CDr 1.0000 0.9695 0.9528 0.9387 0.9268 0.9181
w=0.5: CDr 1.0000 0.9652 0.9446 0.9295 0.9174 0.9093
w=0.3: CDr 1.0000 0.9721 0.9522 0.9378 0.9254 0.9179
w=0.15: CDr 1.0000 0.9688 0.9484 0.9337 0.9241 0.9188
DONE

Excellent! This is very reassuring. The learned C_D velocity response (ratio relative to V=60) for ALL weights (w=1.0, 0.5, 0.3, 0.15) closely tracks the power law V^-0.21 even out to V=90: - power V^-0.21: 1.0, 0.971, 0.954, 0.941, 0.930, 0.918 - w=1.0: 1.0, 0.970, 0.953, 0.939, 0.927, 0.918 ✓ - w=0.5: 1.0, 0.965, 0.945, 0.930, 0.917, 0.909 (slightly steeper) - w=0.3: 1.0, 0.972, 0.952, 0.938, 0.925, 0.918 ✓ (tracks power law very well!) - w=0.15: 1.0, 0.969, 0.948, 0.934, 0.924, 0.919 ✓ So even at w=0.3 and w=0.15, the C_D velocity trend is preserved correctly out to V=90! The augmented rows, even downweighted, successfully teach the trend because they're the ONLY source of information about V>75 behavior, so the network relies on them for the high-V shape. The downweighting mainly reduces the label NOISE impact on the absolute level for in-range cases, while the trend (which is consistent across all augmented samples) still gets learned. This means I can safely use a low aug_cd_weight (0.3) to get the best absolute C_D accuracy (0.138 nrmse) AND preserve the correct extrapolation trend. w=0.3 gives the best extrap mean (0.086) and great trend preservation. w=0.15 also preserves trend but earlier results showed diminishing returns; let me not go too low to keep robustness. Let me reconsider: w=0.3 on the faithful split gave cd=0.138, cl=0.0345, mean=0.086. That's very good. Let me also double-check at w=0.2 and confirm w=0.3 vs 0.5 on a SECOND extrapolation split to ensure robustness (not overfitting to the V<69 split). Let me do a final robustness check: evaluate w ∈ {0.3, 0.5} on TWO splits (V<69 and a more extreme V<62 test V>=70) with the final SWA config, plus in-range val. Then lock in the config. Actually, I've gathered strong evidence. Let me be efficient. Let me settle on: - aug range [35, 92], exp -0.21, n_aug=1, aug_cd_weight=0.3, SWA (epochs 120, swa_start 70), no jitter, w_cd=1.0. But let me do one more validation on a different split to avoid overfitting my choices to the V<69 split, and then build the final model. Let me also reconsider the augmentation vmax for the final: since hidden test could reach ~82-85, augmenting to 92 gives margin. I used 90 in tests; 92 is fine. Let me run a cross-check on a second split (train V<62, test V>=70, a 13-21% extrapolation) comparing w=0.3 and w=0.5, and also re-confirm on V<69. Use 3 reps. This is the final validation before building. Actually, to save time, let me just confirm w=0.3 vs w=0.5 on the second, more extreme split (the V<69 results I already have). If w=0.3 still wins or ties on the more extreme split, I'll use 0.3; if it degrades, I'll use 0.5 as a safer middle. Let me run that.

This is very reassuring. Even at low `aug_cd_weight` (0.3, 0.15), the learned C_D velocity response tracks the V^−0.21 law almost perfectly out to V=90 , the downweighted augmented rows are the *only* source of high-V information, so the network still learns the trend, while downweighting just reduces label-noise impact on the absolute level. So w=0.3 gives the best of both. Let me do a final robustness cross-check on a *more extreme* split (train V<62, test V≥70, a 13–21% extrapolation) to make sure I'm not overfitting my choices to one split.

bash

cd /app && cat > /tmp/sweep4.py << 'EOF'
import sys; sys.path.insert(0,"/app")
import numpy as np, lib
cfg = lib.load_cfg()
pts, tg = lib.load_split("train"); vpts,vtg=lib.load_split("val")
vel = pts[:,0,2]
def evalsplit(thr_tr, thr_te, tag, wlist):
    tp,tt = pts[vel<thr_tr], tg[vel<thr_tr]; ep,et = pts[vel>=thr_te], tg[vel>=thr_te]
    print(f"== split train V<{thr_tr} (n={len(tp)}), test V>={thr_te} (n={len(ep)}, {ep[:,0,2].min():.0f}-{ep[:,0,2].max():.0f}) ==",flush=True)
    for w in wlist:
        E=[]
        for s in range(3):
            m,n=lib.train_model(tp,tt,cfg,seed=s,aug_vmin=35,aug_vmax=92,aug_p=-0.21,epochs=110,swa_start=65,aug_cd_weight=w)
            r=lib.nrmse_report(lib.predict(m,ep,*n),et); E.append((r['mean_nrmse_std'],r['nrmse_std'][0],r['nrmse_std'][1]))
        E=np.array(E).mean(0)
        print(f"  w={w}: mean={E[0]:.4f} cd={E[1]:.4f} cl={E[2]:.4f}",flush=True)
evalsplit(62,70,"extreme",[0.3,0.5])
print("DONE",flush=True)
EOF
nohup python /tmp/sweep4.py > /tmp/sweep4.out 2>&1 &
echo "started $!"

output

started 13958

bash

sleep 295 && cat /tmp/sweep4.out

output

== split train V<62 (n=440), test V>=70 (n=70, 70-75) ==
  w=0.3: mean=0.0852 cd=0.1278 cl=0.0427

On the more extreme split (train V<62, test V≥70, a 13-21% extrapolation), w=0.3 gives mean=0.085, cd=0.128, cl=0.043. Still excellent! C_D is even better here (0.128) , interesting, because this test set (V 70-75) with more training data below might be easier, or variance. Let me wait for w=0.5 to compare.

On the more extreme split, w=0.3 holds up well (mean 0.085, cd 0.128, cl 0.043). Let me confirm against w=0.5.

bash

sleep 160 && cat /tmp/sweep4.out

output

== split train V<62 (n=440), test V>=70 (n=70, 70-75) ==
  w=0.3: mean=0.0852 cd=0.1278 cl=0.0427

bash

sleep 120 && cat /tmp/sweep4.out

output

== split train V<62 (n=440), test V>=70 (n=70, 70-75) ==
  w=0.3: mean=0.0852 cd=0.1278 cl=0.0427
  w=0.5: mean=0.0833 cd=0.1254 cl=0.0412
DONE

On the extreme split: - w=0.3: mean 0.0852, cd 0.128, cl 0.043 - w=0.5: mean 0.0833, cd 0.125, cl 0.041 Essentially tied (w=0.5 marginally better here, w=0.3 marginally better on the V<69 split). Both are excellent and robust. The difference is within noise. I'll choose aug_cd_weight = 0.4 as a robust middle value between 0.3 and 0.5. Now I'm confident in the final configuration: - Augmentation: velocity rescale to [35, 92], C_D ∝ V^-0.21, C_L invariant, n_aug=1, fresh each epoch. - aug_cd_weight = 0.4 (downweight C_D loss on augmented rows). - SWA: ~120 epochs, swa_start ~70. - No jitter. w_cd=1.0. Adam lr 1e-3, wd 1e-4, cosine schedule. - Train on train+val (both in-range), to maximize shape diversity. - Normalization computed over the augmented pool (so velocity std reflects the wide range and test velocities map to reasonable standardized values). Let me reconsider a couple of final details: 1. **Should I include val in final training?** The val set is in-range (V 31-74). Adding 80 more diverse airfoil shapes helps geometry generalization, which benefits both C_D and C_L at all velocities. The scored set is out-of-range in velocity but covers similar airfoil shapes and aoa. More shapes → better. I'll include val. Risk: I lose the ability to validate, but I've already selected the config. I'll do a final sanity check by training on train-only, evaluating on val (in-range) and on a held-out high-V slice, then train the FINAL on train+val. Actually, cleaner: I'll train the final model on train+val combined. But to report a final honest extrapolation estimate, I'll ALSO train one on train-only with a high-V holdout first as a last sanity check. I already have enough evidence though. 2. **Augmentation vmin**: I used 35 (below train min 31). Augmenting to lower V too helps densify. But the test is high-V. Augmenting down to 35 vs train min 31 , fine. Actually, let me make vmin cover the full training range and a bit below, so the network sees a smooth function across [35,92]. Keep vmin=35. Actually, wait: I should also make sure the ORIGINAL low-V cases (V=31-35) are still well-covered. They're in the original data (anchored at full weight). Augmentation adds [35,92]. The combined velocity coverage is [31,92], smooth. Good. 3. **aug exponent**: -0.21 (data-fitted central value). Good. 4. **Final normalization**: compute over the augmented pool of train+val. Good. Let me also reconsider: the de-standardization of C_D. With augmentation, the C_D distribution in the pool shifts toward lower values (high-V augmented cases have lower C_D). This shifts targ_mean/targ_std. That's fine , it's just the output scaling. The network learns to output standardized values that de-standardize to correct C_D. As long as train and inference use the SAME targ_mean/targ_std (they do, from the checkpoint), it's consistent. Now let me also think about whether there's any risk the hidden test has velocities much higher than 82 (e.g., 90+). The prompt says "higher-speed, higher-Reynolds end" and public_test is 75-81.6. I'll augment to 92 which covers a good margin. If the hidden set somehow goes to 85-90, my model still follows the power law correctly (probe confirmed). Good. Let me now also verify the full inference contract: load checkpoint, build model, load state_dict, apply feat/targ norms, predict on public_test, sanity-check the outputs are in physical range. Let me write the final training script and run it. Let me make it train on train+val, with the chosen config, and save to /app/outputs/model.pt. I'll also print diagnostics. Let me set epochs to 130, swa_start=75 for a good SWA window. Actually, SWA window of ~55 epochs (75-130) is good. One more consideration: the batch size. Default 32. With train+val = 710 originals + 710 augmented = 1420 per epoch, bs=32 → ~44 batches/epoch. Fine. I might increase bs slightly for speed but 32 is fine and was used in all experiments. Let me also reconsider w_cd. The metric is mean of cd a …[truncated]

Both weights are essentially tied and robust across splits (w=0.3 best on the mild split, w=0.5 marginally best on the extreme split). I'll pick **aug_cd_weight=0.4** as the robust middle. My final configuration is locked: - Velocity augmentation to [35, 92], C_D ∝ V^−0.21, C_L invariant, n_aug=1, refreshed each epoch - aug_cd_weight=0.4 (originals anchor absolute C_D; augmented teach the trend + C_L invariance) - SWA over a long tail, no jitter, equal target weights - Train on train+val (both in-range) for maximum shape diversity; normalization fit over the augmented pool Let me do one last honest end-to-end validation of this exact config (train-only, held-out high-V slice) before building the final checkpoint.

bash

cd /app && cat > /tmp/final_val.py << 'EOF'
import sys; sys.path.insert(0,"/app")
import numpy as np, lib
cfg = lib.load_cfg()
pts, tg = lib.load_split("train"); vpts,vtg=lib.load_split("val")
vel = pts[:,0,2]; thr=69.0
tp,tt = pts[vel<thr], tg[vel<thr]; ep,et = pts[vel>=thr], tg[vel>=thr]
CFG=dict(aug_vmin=35,aug_vmax=92,aug_p=-0.21,epochs=125,swa_start=72,aug_cd_weight=0.4,n_aug=1)
E=[];V=[]
for s in range(4):
    m,n=lib.train_model(tp,tt,cfg,seed=s,**CFG)
    r=lib.nrmse_report(lib.predict(m,ep,*n),et); rv=lib.nrmse_report(lib.predict(m,vpts,*n),vtg)
    E.append((r['mean_nrmse_std'],r['nrmse_std'][0],r['nrmse_std'][1],r['nrmse_rng'][0],r['nrmse_rng'][1],r['cd_nmae_mean']))
    V.append(rv['mean_nrmse_std'])
E=np.array(E); m=E.mean(0); sd=E.std(0)
print(f"FINAL CONFIG, held-out V>=69 (4 seeds):")
print(f"  mean_nrmse_std={m[0]:.4f}+-{sd[0]:.4f}  cd={m[1]:.4f}+-{sd[1]:.4f}  cl={m[2]:.4f}+-{sd[2]:.4f}")
print(f"  nrmse_rng cd={m[3]:.4f} cl={m[4]:.4f} | cd_nmae={m[5]:.4f} | in-range val mean={np.mean(V):.4f}")
print("DONE",flush=True)
EOF
nohup python /tmp/final_val.py > /tmp/final_val.out 2>&1 &
echo "started $!"

output

started 14020

While that validation runs, let me write the final training script that produces the deliverable checkpoint.

str_replace_based_edit_tool

command: create
path: /app/train_final.py
file_text: """Final trainer for the airfoil force-coefficient PointNet.

Strategy (see analysis): the scored cases sit at higher inlet velocity than any
training case. The data obeys a clean physics law -- C_D ~ V^(-0.21) (turbulent
skin-friction scaling) while C_L is velocity-independent. We exploit this with a
physics-informed velocity augmentation that rescales each case's inlet velocity
across a wide range [35, 92] m/s, scaling C_D by the power law and holding C_L
fixed. This turns the velocity extrapolation into interpolation: it teaches the
net the correct decreasing-C_D trend and the velocity-invariance of C_L, instead
of the spurious upward drift a plain PointNet extrapolates into.

C_D loss on augmented rows is down-weighted (0.4) so the real cases anchor the
absolute C_D level while the augmented rows still supply the (consistent) trend.
SWA over the training tail stabilizes the single checkpoint we must ship. The
architecture is untouched; only the four normalization tensors + state_dict are
saved.
"""
from __future__ import annotations

import os
import sys
from pathlib import Path

import numpy as np
import torch

APP_DIR = Path(os.environ.get("APP_DIR", "/app"))
sys.path.insert(0, str(APP_DIR))
import lib  # noqa: E402

CONFIG = dict(
    aug_vmin=35.0, aug_vmax=92.0, aug_p=-0.21,
    n_aug=1, aug_cd_weight=0.4,
    epochs=130, swa_start=74,
    bs=32, lr=1e-3, wd=1e-4, w_cd=1.0, w_cl=1.0,
    jitter=0.0,
)


def main() -> None:
    cfg = lib.load_cfg()
    torch.manual_seed(int(cfg["seed"]))

    tr_pts, tr_tg = lib.load_split("train")
    va_pts, va_tg = lib.load_split("val")
    # Train on train+val: both are in-range; the extra airfoil shapes improve
    # geometry generalization, which helps C_D/C_L at every velocity.
    pts = torch.cat([tr_pts, va_pts], 0)
    tg = torch.cat([tr_tg, va_tg], 0)
    print(f"[final] training cases: {pts.shape[0]} (train {tr_pts.shape[0]} + val {va_pts.shape[0]})", flush=True)
    print(f"[final] config: {CONFIG}", flush=True)

    model, norms = lib.train_model(pts, tg, cfg, seed=int(cfg["seed"]), **CONFIG)
    fmean, fstd, tmean, tstd = norms
    print(f"[final] feat_mean={fmean.tolist()}")
    print(f"[final] feat_std ={fstd.tolist()}")
    print(f"[final] targ_mean={tmean.tolist()} targ_std={tstd.tolist()}")

    # Report fit on the available (in-range) labeled data for sanity.
    for name, p, t in [("train", tr_pts, tr_tg), ("val", va_pts, va_tg)]:
        r = lib.nrmse_report(lib.predict(model, p, *norms), t)
        print(f"[final] {name}: mean_nrmse_std={r['mean_nrmse_std']:.4f} "
              f"cd={r['nrmse_std'][0]:.4f} cl={r['nrmse_std'][1]:.4f}", flush=True)

    out = APP_DIR / "outputs" / "model.pt"
    lib.save_checkpoint(model, norms, out)
    print(f"[final] wrote {out}", flush=True)


if __name__ == "__main__":
    main()

output

New file created successfully at /app/train_final.py

bash

sleep 180 && cat /tmp/final_val.out

output

<system>Tool ran without output or errors</system>

The final_val is 4 seeds * 125 epochs SWA ~ 4*90 = 360s. Let me wait more.

The 4-seed validation needs more time. Let me wait.

bash

sleep 200 && cat /tmp/final_val.out

output

<system>Tool ran without output or errors</system>

bash

ps aux | grep "[f]inal_val" >/dev/null && echo running; sleep 60; cat /tmp/final_val.out

output

running

bash

sleep 90 && cat /tmp/final_val.out

output

FINAL CONFIG, held-out V>=69 (4 seeds):
  mean_nrmse_std=0.1043+-0.0305  cd=0.1755+-0.0635  cl=0.0331+-0.0032
  nrmse_rng cd=0.0374 cl=0.0093 | cd_nmae=0.0258 | in-range val mean=0.0959
DONE

4-seed average: - mean_nrmse_std = 0.104 ± 0.031 - cd = 0.176 ± 0.064 (high variance!) - cl = 0.033 ± 0.003 (very stable, low) - nrmse_rng: cd 0.037, cl 0.009 (if the metric normalizes by range, these are the numbers , very good) - cd_nmae = 0.026 (C_D normalized MAE , 2.6%, good) - in-range val = 0.096 The cd nrmse_std has high variance (0.176 ± 0.064) across seeds. The individual seeds range quite a bit. This is concerning for a single-checkpoint submission , one bad seed could give cd~0.24. Wait, but each of these 4 runs already uses SWA (averaging along trajectory), yet still varies seed-to-seed by ±0.064. The SWA reduces within-trajectory noise but not the between-init basin variance. The mean here (0.104) is a bit higher than the 3-seed sweep3 w0.3 result (0.086) , different random seeds and the extra variance. The cd variance is the issue. Hmm, the cd_nmae (0.026) and nrmse_rng (0.037) look great though. The nrmse_std is sensitive because it divides by the std of the held-out cd, which for a narrow high-V slice might be small, amplifying the metric. The actual scored metric normalizer is unknown. Given the high cd variance across seeds, I should reduce it for the final single checkpoint. Options: 1. **Train multiple seeds and average predictions** , but I can only submit ONE model. Can't ensemble at inference. 2. **Longer SWA / more epochs** to converge more consistently. 3. **Pick the best seed by in-range val** , but in-range val doesn't predict extrap cd well. 4. **Larger SWA window or SWA across a cyclic LR** to average more diverse points in the same basin. 5. **Weight averaging across multiple independent seeds** , generally doesn't work for independently initialized nets (different basins, permutation symmetry), so averaging weights would be bad. Actually, the variance might be partly from the small held-out set (80 cases, V 69-75) and the nrmse_std normalization. The real test has 100+ cases over V 75-82. Let me not over-index on the std-normalized cd variance. But reducing variance is still good. Let me think about the most robust single model: Option A: Train longer with a bigger SWA window. More SWA samples → smoother, more robust weights. Option B: Reduce model variance via more data (train+val, which the final uses , 710 vs 550 here). More data reduces variance. The final model uses train+val (710 originals), which should be more stable than this validation (550). Option C: Lower LR in the SWA phase or constant LR SWA. The cosine schedule drives LR→0 at the end; SWA from epoch 74-130 averages over decreasing LR. A common SWA recipe uses a higher constant LR during averaging to sample diverse points. But averaging over the cosine tail (what I do) is also fine and gives stable late-training weights. Let me reconsider the variance source. Actually, cd nrmse 0.176 ± 0.064 , the std 0.064 is large relative to mean. Let me look at whether it's one outlier seed. Let me also consider that averaging MORE seeds' PREDICTIONS is not allowed, but I could train the final model and just accept it. Actually, the cleanest variance reduction available to me within the single-checkpoint constraint: **train on more data (train+val) + long SWA**. Let me also consider increasing the SWA window and maybe doing 2 independent runs and keeping the one with better in-range val as a tiebreaker , but in-range val (cl-dominated since cl is stable, cd in-range easier) may not correlate with extrap cd. Let me check: does in-range val correlate with held-out extrap cd across seeds? If yes, I can select the best seed by val. Let me look at per-seed numbers. The final_val script only printed the mean. Let me re-run a quick per-seed diagnostic to see the correlation and variance, using train+val-sized training (to match final) , actually that removes the held-out set. Alternative plan for robustness: Since I can't ensemble at inference, but the variance is concerning, let me consider whether a SWA with MORE averaging (longer …[truncated]

The 4-seed validation shows strong, stable results on the robust metrics (cd_nmae=2.6%, nrmse_rng cd=0.037, cl=0.009; C_L nrmse very stable at 0.033±0.003). However, the **std-normalized C_D shows high seed variance** (0.176±0.064), which matters since I ship a single checkpoint. Let me check whether a longer SWA window reduces that variance.

bash

cd /app && cat > /tmp/swa_test.py << 'EOF'
import sys; sys.path.insert(0,"/app")
import numpy as np, lib
cfg = lib.load_cfg()
pts, tg = lib.load_split("train")
vel = pts[:,0,2]; thr=69.0
tp,tt = pts[vel<thr], tg[vel<thr]; ep,et = pts[vel>=thr], tg[vel>=thr]
def test(tag, **kw):
    C=[]
    for s in range(4):
        m,n=lib.train_model(tp,tt,cfg,seed=s,aug_vmin=35,aug_vmax=92,aug_p=-0.21,aug_cd_weight=0.4,**kw)
        r=lib.nrmse_report(lib.predict(m,ep,*n),et); C.append(r['nrmse_std'][0])
    C=np.array(C); print(f"[{tag}] cd mean={C.mean():.4f} std={C.std():.4f} min={C.min():.4f} max={C.max():.4f}",flush=True)
test("ep125 swa72", epochs=125, swa_start=72)
test("ep170 swa85", epochs=170, swa_start=85)
print("DONE",flush=True)
EOF
nohup python /tmp/swa_test.py > /tmp/swa_test.out 2>&1 &
echo "started $!"

output

started 16101

bash

sleep 290 && cat /tmp/swa_test.out

output

<system>Tool ran without output or errors</system>

bash

sleep 200 && cat /tmp/swa_test.out

output

<system>Tool ran without output or errors</system>

Still running (8 runs total, the ep170 ones are slow). Let me wait more.

bash

sleep 180 && cat /tmp/swa_test.out

output

[ep125 swa72] cd mean=0.1755 std=0.0635 min=0.1207 max=0.2779

ep125 swa72: cd mean 0.176, std 0.064, range [0.12, 0.28]. Matches the earlier 4-seed result. Let me wait for ep170 swa85.

bash

sleep 160 && cat /tmp/swa_test.out

output

[ep125 swa72] cd mean=0.1755 std=0.0635 min=0.1207 max=0.2779

bash

sleep 120 && cat /tmp/swa_test.out | tail -2

output

[ep125 swa72] cd mean=0.1755 std=0.0635 min=0.1207 max=0.2779

bash

sleep 120 && cat /tmp/swa_test.out | tail -2

output

[ep125 swa72] cd mean=0.1755 std=0.0635 min=0.1207 max=0.2779

The ep170 run is taking very long. Let me check if it's still running.

bash

ps aux | grep "[s]wa_test" >/dev/null && echo "running"; sleep 90; cat /tmp/swa_test.out | tail -2

output

running
[ep125 swa72] cd mean=0.1755 std=0.0635 min=0.1207 max=0.2779

bash

sleep 120 && cat /tmp/swa_test.out | tail -2

output

[ep170 swa85] cd mean=0.1526 std=0.0397 min=0.1233 max=0.2201
DONE

Longer SWA (ep170, swa85, 85-epoch window) helps: - cd mean 0.176 → 0.153 - cd std 0.064 → 0.040 (variance reduced!) - range [0.12, 0.22] vs [0.12, 0.28] (worst case improved from 0.28 to 0.22) So a longer SWA window both improves the mean AND reduces variance. Great. This is the robustness I wanted. Let me use an even longer schedule for the final, but balance training time. ep170 took ~a long time per seed (~2min). For the final single model I can afford that. Let me consider going further: ep200, swa100. But diminishing returns and time. Let me use epochs=180, swa_start=90 (90-epoch SWA window) for the final , good balance. Actually, let me reconsider the schedule. The cosine annealing goes to 0 at epochs. With swa_start=90 and epochs=180, SWA averages over epochs 90-180 where LR goes from ~0.5*lr down to 0. That's a wide window of converged-ish models. Good. Now, the final model trains on train+val (710 originals → 1420 with augmentation per epoch). That's more data than the 550 used in validation, so variance should be even lower and accuracy better. Let me finalize. I'll update train_final.py CONFIG to epochs=180, swa_start=90. Let me also reconsider: is there any further robustness gain from reducing the C_D variance via the loss or the exponent? The variance comes from the geometry→C_D mapping being hard to pin down (shape-dependent drag), and different seeds learn slightly different mappings. More data (train+val) and more SWA are the main levers I have. I've applied both. Let me also double check the worst-case seed behavior is acceptable. With ep170, worst seed cd=0.22. On train+val with ep180, should be better. Let me update train_final.py and run it to produce the final checkpoint. Then I'll validate the checkpoint loads correctly and produces sane predictions on public_test. Let me update the config.

A longer SWA window clearly helps both accuracy and robustness: cd mean 0.176→0.153 and **std 0.064→0.040** (worst-case seed 0.28→0.22). Since the final model trains on more data (train+val) and ships once, I'll use an even longer schedule. Let me update the final config and build the checkpoint.

str_replace_based_edit_tool

command: str_replace
path: /app/train_final.py
old_str:     epochs=130, swa_start=74,
new_str:     epochs=185, swa_start=90,

output

The file /app/train_final.py has been edited successfully.

bash

cd /app && nohup python train_final.py > /tmp/train_final.out 2>&1 &
echo "started $!"

output

started 20940

While the final checkpoint trains, let me write a standalone verification script that mimics the grader's inference contract exactly (load checkpoint → build model → load state_dict → standardize → forward → de-standardize).

str_replace_based_edit_tool

command: create
path: /app/verify_checkpoint.py
file_text: """Verify the saved checkpoint loads into the pinned architecture and runs,
mimicking the grader's inference contract exactly."""
from __future__ import annotations

import json
import sys
from pathlib import Path

import torch

APP = Path("/app")
sys.path.insert(0, str(APP))
from candidate_model import build_model
from dataset import load_split


def main() -> None:
    cfg = json.load((APP / "task_config.json").open())
    ckpt = torch.load(APP / "outputs" / "model.pt", map_location="cpu", weights_only=True)

    # contract: all four norm tensors present with correct shapes
    for k in ["state_dict", "feat_mean", "feat_std", "targ_mean", "targ_std"]:
        assert k in ckpt, f"missing key {k}"
    assert ckpt["feat_mean"].shape == (4,) and ckpt["feat_std"].shape == (4,)
    assert ckpt["targ_mean"].shape == (2,) and ckpt["targ_std"].shape == (2,)
    assert torch.isfinite(ckpt["feat_std"]).all() and (ckpt["feat_std"] > 0).all()
    assert torch.isfinite(ckpt["targ_std"]).all() and (ckpt["targ_std"] > 0).all()

    # load into the exact pinned architecture (strict)
    model = build_model(cfg)
    missing = model.load_state_dict(ckpt["state_dict"], strict=True)
    print("[verify] state_dict loaded strict=True:", missing)
    model.eval()

    fmean, fstd = ckpt["feat_mean"], ckpt["feat_std"]
    tmean, tstd = ckpt["targ_mean"], ckpt["targ_std"]

    def infer(pts):
        x = (pts - fmean.view(1, 1, -1)) / fstd.view(1, 1, -1)
        with torch.no_grad():
            out = model(x)
        return out * tstd.view(1, -1) + tmean.view(1, -1)

    # run on public_test (no labels) -- must be finite and physical
    pts, _ = load_split(APP / "data" / "public_test")
    pred = infer(pts)
    assert torch.isfinite(pred).all(), "non-finite predictions!"
    cd, cl = pred[:, 0], pred[:, 1]
    print(f"[verify] public_test n={len(pts)} V range {pts[:,0,2].min():.1f}-{pts[:,0,2].max():.1f}")
    print(f"[verify] pred C_D: min={cd.min():.5f} max={cd.max():.5f} mean={cd.mean():.5f}")
    print(f"[verify] pred C_L: min={cl.min():.4f} max={cl.max():.4f} mean={cl.mean():.4f}")
    # physical sanity: C_D positive and in a plausible band, C_L in training span
    assert (cd > 0).all(), "negative drag!"
    assert cd.max() < 0.1 and cd.min() > 0.002, "C_D out of plausible band"
    assert cl.min() > -1.5 and cl.max() < 3.0, "C_L out of plausible band"

    # report metrics on the labeled val split through the exact same path
    vpts, vtg = load_split(APP / "data" / "val")
    vp = infer(vpts)
    err = vp - vtg
    rmse = err.pow(2).mean(0).sqrt()
    nrmse = rmse / vtg.std(0)
    print(f"[verify] val nrmse_std: cd={nrmse[0]:.4f} cl={nrmse[1]:.4f} mean={nrmse.mean():.4f}")
    print("[verify] OK - checkpoint satisfies the inference contract.")


if __name__ == "__main__":
    main()

output

New file created successfully at /app/verify_checkpoint.py

bash

sleep 120 && cat /tmp/train_final.out

output

[final] training cases: 710 (train 630 + val 80)
[final] config: {'aug_vmin': 35.0, 'aug_vmax': 92.0, 'aug_p': -0.21, 'n_aug': 1, 'aug_cd_weight': 0.4, 'epochs': 185, 'swa_start': 90, 'bs': 32, 'lr': 0.001, 'wd': 0.0001, 'w_cd': 1.0, 'w_cl': 1.0, 'jitter': 0.0}

Training is running (185 epochs on 1420 augmented cases). This will take a few minutes. Let me wait.

Final training is underway (185 epochs on 1420 augmented cases). Let me wait for it to complete.

bash

sleep 280 && cat /tmp/train_final.out

output

[final] training cases: 710 (train 630 + val 80)
[final] config: {'aug_vmin': 35.0, 'aug_vmax': 92.0, 'aug_p': -0.21, 'n_aug': 1, 'aug_cd_weight': 0.4, 'epochs': 185, 'swa_start': 90, 'bs': 32, 'lr': 0.001, 'wd': 0.0001, 'w_cd': 1.0, 'w_cl': 1.0, 'jitter': 0.0}
[final] feat_mean=[0.38364872336387634, 0.007897475734353065, 57.995487213134766, 0.07678443193435669]
[final] feat_std =[0.395596981048584, 0.033333562314510345, 15.351306915283203, 0.09548091143369675]
[final] targ_mean=[0.012543597258627415, 0.6618618965148926] targ_std=[0.005036944057792425, 0.5780647397041321]
[final] train: mean_nrmse_std=0.0846 cd=0.1426 cl=0.0265
[final] val: mean_nrmse_std=0.0592 cd=0.0927 cl=0.0256
[final] wrote /app/outputs/model.pt

The final model is trained and saved. Key points: - feat_mean velocity = 58.0, feat_std velocity = 15.35 (reflects the augmented wide range [35,92]+originals). So at inference, test V=81 → (81-58)/15.35 = +1.5 std , well within the trained range (since augmentation went to 92 → (92-58)/15.35 = +2.2 std). Good, test velocities are interpolation now, not extrapolation. - train fit: mean_nrmse_std 0.085 (cd 0.143, cl 0.027) - val fit (in-range): mean 0.059 (cd 0.093, cl 0.026) , but val was included in training, so this is optimistic. Still, it's a sanity check that the model fits. Now let me verify the checkpoint with the verification script.

The final checkpoint is saved. Note the velocity normalization (mean 58.0, std 15.35) now spans the augmented range , so test velocities of 75–82 map to only +1.1 to +1.6 std (well within the augmented training span up to +2.2σ), confirming extrapolation became interpolation. Let me run the verification script to confirm the inference contract.

bash

cd /app && python verify_checkpoint.py

output

[verify] state_dict loaded strict=True: <All keys matched successfully>
[verify] public_test n=100 V range 75.0-81.6
[verify] pred C_D: min=0.00718 max=0.02951 mean=0.01194
[verify] pred C_L: min=-0.5056 max=1.7097 mean=0.6751
[verify] val nrmse_std: cd=0.0927 cl=0.0256 mean=0.0592
[verify] OK - checkpoint satisfies the inference contract.

The checkpoint: - Loads strict=True into the pinned architecture. ✓ - Produces finite, physical predictions on public_test (V 75-81.6). C_D in [0.0072, 0.0295], mean 0.0119 (physical); C_L in [-0.51, 1.71], mean 0.675 (matches training target distribution). ✓ - All contract keys present with correct shapes. ✓ Now let me sanity check the public_test predictions against the physics expectation. The public_test is at high velocity (mean 78.2). Expected C_D should be slightly LOWER than training average (since higher velocity → lower C_D). Training C_D mean was 0.0128; public_test pred mean 0.0119 , yes, lower, consistent with the V^-0.21 trend. Let me do a final validation: compare the public_test predictions to the physics model prediction (aoa + V^-0.21 fitted on train) to make sure they're in the right ballpark, and check the velocity response of the FINAL model is monotonic decreasing (correct extrapolation). This is my last confidence check. Let me verify the final model's velocity response and compare public_test predictions to the physics baseline.

The checkpoint satisfies the full contract: loads `strict=True`, produces finite and physically plausible predictions, and the public_test C_D mean (0.0119) is correctly *below* the training mean (0.0128) as the V^−0.21 trend predicts. Let me do a final confidence check: confirm the shipped model's velocity response is monotonic-decreasing (correct extrapolation direction) and that its public_test predictions agree with an independent physics-based estimate.

bash

cd /app && python -c "
import sys; sys.path.insert(0,'/app'); import torch, numpy as np, json
from candidate_model import build_model
from dataset import load_split
cfg=json.load(open('task_config.json'))
ck=torch.load('outputs/model.pt',weights_only=True)
m=build_model(cfg); m.load_state_dict(ck['state_dict']); m.eval()
fm,fs,tm,ts=ck['feat_mean'],ck['feat_std'],ck['targ_mean'],ck['targ_std']
def infer(p):
    x=(p-fm.view(1,1,-1))/fs.view(1,1,-1)
    with torch.no_grad(): o=m(x)
    return o*ts.view(1,-1)+tm.view(1,-1)
# velocity response of FINAL model on 40 public_test airfoils
pt,_=load_split('data/public_test')
sel=pt[:40].clone()
print('Final model C_D velocity response (median over 40 test airfoils):')
prev=None; vs=[60,70,75,80,85,90,95]
row=[]
for v in vs:
    s=sel.clone(); s[:,:,2]=v; row.append(infer(s)[:,0].median().item())
print('  V  :',' '.join(f'{v:7d}' for v in vs))
print('  C_D:',' '.join(f'{c:7.5f}' for c in row))
print('  mono-decreasing:', all(row[i]>row[i+1] for i in range(len(row)-1)))
# Compare public_test preds to physics model fitted on train
tr_p,tr_t=load_split('data/train')
aoa=tr_p[:,0,3].numpy(); lv=np.log(tr_p[:,0,2].numpy()); lcd=np.log(tr_t[:,0].numpy())
X=np.stack([np.ones_like(aoa),aoa,aoa**2,lv],1); b,*_=np.linalg.lstsq(X,lcd,rcond=None)
pa=pt[:,0,3].numpy(); plv=np.log(pt[:,0,2].numpy())
Xp=np.stack([np.ones_like(pa),pa,pa**2,plv],1); phys=np.exp(Xp@b)
net=infer(pt)[:,0].numpy()
print()
print('public_test C_D: net mean=%.5f  physics mean=%.5f  corr=%.3f'%(net.mean(),phys.mean(),np.corrcoef(net,phys)[0,1]))
print('  median |net-phys|/phys = %.1f%%'%(100*np.median(np.abs(net-phys)/phys)))
"

output

Final model C_D velocity response (median over 40 test airfoils):
  V  :      60      70      75      80      85      90      95
  C_D: 0.01030 0.00998 0.00985 0.00972 0.00961 0.00943 0.00925
  mono-decreasing: True

public_test C_D: net mean=0.01194  physics mean=0.01155  corr=0.936
  median |net-phys|/phys = 7.9%

The final shipped model: 1. **C_D velocity response is monotonic decreasing** from V=60 to V=95 (0.0103 → 0.00925), correctly following the physics even beyond the test range (up to 95). No spurious upturn. ✓ 2. **public_test C_D predictions correlate 0.936 with the independent physics model**, with median relative difference of 7.9%. The net mean (0.0119) is close to physics (0.0116). The net uses full geometry so it legitimately differs from the aoa-only physics model by the shape-dependent drag , which is exactly the value-add. ✓ Everything is consistent and robust. The model is doing exactly what the physics demands. Let me now reconsider if there's anything I can do to further improve, given remaining budget. The main remaining uncertainty is the C_D seed variance. My final model used a long SWA window on train+val (more data), which should place it near the better end. But it's a single draw. Let me quantify where this particular final checkpoint likely falls. I can't directly measure extrapolation on the real test (no labels). But I did measure on held-out V≥69 that ep170/swa85 gives cd 0.153±0.040. The final uses ep185/swa90 on MORE data (train+val), so likely similar or better variance. One thing I could do to hedge against a bad draw: train a few final candidates (different seeds) on train+val, and select the one whose predictions are most "central" / robust. But I have no extrapolation labels to select on. I could select by agreement with the physics model on public_test? That's a reasonable unsupervised selection criterion , the model whose public_test C_D best correlates with physics AND whose C_L is most velocity-stable. But that might bias toward the physics model rather than the true (geometry-aware) answer. Risky. Alternatively, I could reduce variance by averaging predictions of multiple seeds , but single checkpoint only. Actually, the cleanest robustness improvement within constraints: I already use SWA. The final is reasonable. Let me not over-engineer. However, let me reconsider one more time whether the current final checkpoint is a "good draw" by checking its val (in-range) and its consistency. The val nrmse (cd 0.093) is in-range and optimistic (val was in training). Not a great selector. Let me think about whether to retrain the final with the seed that's known-good. I used seed=cfg['seed']=20260518. That's an arbitrary seed. Its quality is unknown for extrapolation. Given the variance (cd std 0.04 on held-out), different seeds of the final could give cd from ~0.12 to ~0.20 on a held-out slice. To reduce the risk of a bad draw, let me train 3 final candidates (seeds) on train+val and pick by a principled criterion. What criterion without extrapolation labels? Options: (a) Agreement with physics on public_test C_D (corr + level) , but I want geometry-awareness too. (b) Lowest in-range val error , but val is in training now. (c) Train on train-only (hold out val as in-range check) for selection, then... no, I want val in training. Hmm. Actually, a robust and defensible approach: since I CAN train on train-only and evaluate the extrapolation proxy (V≥69 held-out from train), I could select the seed that does best on a held-out-high-V slice, THEN retrain that seed on all data. But the seed's behavior changes when data changes, so selection on a sub-split doesn't transfer cleanly to the full-data model. Given diminishing returns and the solid physics-grounded behavior already confirmed, I think the current checkpoint is good. But let me do ONE thing to improve robustness: train a small number (say 3) of full train+val models with different seeds and AVERAGE THEIR PREDICTIONS is not allowed... Wait , actually, can I make the single model BE an average? SWA already averages within a trajectory. Averaging weights across different random inits generally fails due to permutation symmetry. So no. Let me instead verify the current checkpoint isn't a bad draw by comparing its public_test predict …[truncated]

The shipped model behaves exactly as physics demands: its C_D response is **monotonic-decreasing all the way to V=95** (no spurious upturn), and its public_test predictions correlate 0.936 with an independent physics estimate while adding legitimate geometry-dependent variation. The one remaining risk is seed variance in C_D , since I ship a single checkpoint, a bad draw is possible. To guard against that, I'll train several candidates on train+val and select the one **closest to the cross-seed consensus** (an unsupervised robustness criterion that avoids shipping an outlier). Let me implement this selection.

str_replace_based_edit_tool

command: create
path: /app/select_final.py
file_text: """Train K candidates on train+val and ship the one closest to the cross-seed
consensus on the (unlabeled) public_test cases. Averaging predictions across
independent inits is not allowed (single checkpoint), but the candidate nearest
the consensus is the least likely to be an outlier draw -- a robust way to pick
one model given the C_D seed variance."""
from __future__ import annotations

import json
import sys
from pathlib import Path

import numpy as np
import torch

APP = Path("/app")
sys.path.insert(0, str(APP))
import lib
from candidate_model import build_model
from dataset import load_split

CONFIG = dict(
    aug_vmin=35.0, aug_vmax=92.0, aug_p=-0.21,
    n_aug=1, aug_cd_weight=0.4,
    epochs=185, swa_start=90,
    bs=32, lr=1e-3, wd=1e-4, w_cd=1.0, w_cl=1.0, jitter=0.0,
)
SEEDS = [20260518, 1, 2, 7, 13]


def main() -> None:
    cfg = lib.load_cfg()
    tr_pts, tr_tg = lib.load_split("train")
    va_pts, va_tg = lib.load_split("val")
    pts = torch.cat([tr_pts, va_pts], 0)
    tg = torch.cat([tr_tg, va_tg], 0)
    test_pts, _ = load_split(APP / "data" / "public_test")

    preds, ckpts, mono = [], [], []
    vs = [60, 70, 75, 80, 85, 90]
    sel = test_pts[:40].clone()
    for sd in SEEDS:
        model, norms = lib.train_model(pts, tg, cfg, seed=sd, **CONFIG)
        p = lib.predict(model, test_pts, *norms)
        preds.append(p.numpy())
        # record state for saving
        fmean, fstd, tmean, tstd = norms
        ckpts.append({
            "state_dict": {k: v.cpu() for k, v in model.state_dict().items()},
            "feat_mean": fmean, "feat_std": fstd, "targ_mean": tmean, "targ_std": tstd,
        })
        # monotonic C_D velocity response check
        row = []
        for v in vs:
            s = sel.clone(); s[:, :, 2] = v
            row.append(lib.predict(model, s, *norms)[:, 0].median().item())
        is_mono = all(row[i] > row[i + 1] for i in range(len(row) - 1))
        mono.append(is_mono)
        rv = lib.nrmse_report(lib.predict(model, va_pts, *norms), va_tg)
        print(f"[seed {sd}] mono_cd={is_mono} val_cd={rv['nrmse_std'][0]:.4f} "
              f"test_cd_mean={p[:,0].mean():.5f} test_cl_mean={p[:,1].mean():.4f}", flush=True)

    preds = np.stack(preds, 0)  # [K, N, 2]
    consensus = np.median(preds, 0)  # [N, 2]
    # per-target scale for normalization
    scale = np.array([consensus[:, 0].std(), consensus[:, 1].std()])
    scale = np.clip(scale, 1e-8, None)
    dev = np.sqrt((((preds - consensus[None]) / scale[None, None]) ** 2).mean(axis=(1, 2)))
    print("[select] per-seed deviation from consensus:",
          {SEEDS[i]: round(float(dev[i]), 4) for i in range(len(SEEDS))}, flush=True)

    # restrict to monotonic models, then pick the one closest to consensus
    order = [i for i in np.argsort(dev) if mono[i]] or list(np.argsort(dev))
    best = order[0]
    print(f"[select] chosen seed={SEEDS[best]} (dev={dev[best]:.4f}, mono={mono[best]})", flush=True)

    out = APP / "outputs" / "model.pt"
    torch.save(ckpts[best], out)
    print(f"[select] wrote {out}", flush=True)


if __name__ == "__main__":
    main()

output

New file created successfully at /app/select_final.py

Before running 5 candidates (which will take ~20 min), let me reconsider whether the consensus-selection is sound. The consensus (median across seeds) is the most robust point estimate. Picking the model closest to consensus avoids outliers. This is defensible. And I also gate on monotonic C_D (physics correctness). Good. One concern: 5 seeds at ep185 on 1420 cases ≈ 5 × ~4min = 20min. Each tool call can wait ~290s. I'll launch in background and poll. Let me reduce to a level that still finishes reasonably. 5 seeds is fine; I'll poll a few times. Let me kick it off in the background. Actually, let me reconsider: I'll keep the current model.pt as a backup in case something goes wrong, by copying it first.

The consensus-selection approach is sound: the median across seeds is the most robust estimate, choosing the nearest candidate avoids outliers, and I gate on the physics-correct monotonic-C_D property. Let me back up the current checkpoint first, then run the selection in the background.

bash

cd /app && cp outputs/model.pt outputs/model_backup_seed20260518.pt && ls -la outputs/ && nohup python select_final.py > /tmp/select.out 2>&1 & echo "started $!"

output

started 21826

bash

sleep 290 && cat /tmp/select.out

output

[seed 20260518] mono_cd=True val_cd=0.0927 test_cd_mean=0.01194 test_cl_mean=0.6751

One seed done in ~290s (includes the monotonic check and val eval). 5 seeds ≈ 25 min. Let me keep polling. The first seed (the current final) has mono=True, test_cd_mean 0.0119. Let me wait for the rest.

bash

sleep 290 && cat /tmp/select.out

output

[seed 20260518] mono_cd=True val_cd=0.0927 test_cd_mean=0.01194 test_cl_mean=0.6751
[seed 1] mono_cd=True val_cd=0.0605 test_cd_mean=0.01187 test_cl_mean=0.6699
[seed 2] mono_cd=True val_cd=0.0743 test_cd_mean=0.01182 test_cl_mean=0.6697

Three seeds done, all monotonic: - seed 20260518: test_cd_mean 0.01194, test_cl_mean 0.6751 - seed 1: test_cd_mean 0.01187, test_cl_mean 0.6699 - seed 2: test_cd_mean 0.01182, test_cl_mean 0.6697 The test_cd_mean is very consistent across seeds (0.01182-0.01194), and test_cl_mean too (0.670-0.675). So the aggregate predictions are stable across seeds , the variance I worried about (on the V≥69 held-out slice) is more about per-case scatter than the overall level. Good sign. All monotonic. Let me wait for seeds 7 and 13, then it'll select.

All candidates so far are monotonic and remarkably consistent at the aggregate level (test C_D mean 0.0118–0.0119, C_L mean 0.670–0.675) , the per-case variance I was guarding against doesn't destabilize the overall predictions. Let me wait for the final two seeds and the selection.

bash

sleep 290 && cat /tmp/select.out

output

[seed 20260518] mono_cd=True val_cd=0.0927 test_cd_mean=0.01194 test_cl_mean=0.6751
[seed 1] mono_cd=True val_cd=0.0605 test_cd_mean=0.01187 test_cl_mean=0.6699
[seed 2] mono_cd=True val_cd=0.0743 test_cd_mean=0.01182 test_cl_mean=0.6697
[seed 7] mono_cd=True val_cd=0.0683 test_cd_mean=0.01185 test_cl_mean=0.6734
[seed 13] mono_cd=True val_cd=0.0612 test_cd_mean=0.01187 test_cl_mean=0.6713
[select] per-seed deviation from consensus: {20260518: 0.0313, 1: 0.0417, 2: 0.0286, 7: 0.0197, 13: 0.0171}
[select] chosen seed=13 (dev=0.0171, mono=True)
[select] wrote /app/outputs/model.pt

All 5 seeds monotonic. Deviations from consensus: - seed 20260518: 0.0313 - seed 1: 0.0417 - seed 2: 0.0286 - seed 7: 0.0197 - seed 13: 0.0171 ← chosen (closest to consensus, most central/robust) Seed 13 was selected: it's closest to the consensus across all 5 models, and it's monotonic. It also had a good val_cd (0.0612). This is the most robust single choice. Now let me verify the new model.pt (seed 13) passes the verification contract and check its velocity response.

Selection complete. All 5 candidates are monotonic; **seed 13** is closest to the cross-seed consensus (deviation 0.017 vs. up to 0.042) and is now the shipped model , the most central, least-outlier-prone choice. Let me verify the final checkpoint against the contract and re-confirm its physics behavior.

bash

cd /app && python verify_checkpoint.py && echo "=== velocity response ===" && python -c "
import sys; sys.path.insert(0,'/app'); import torch, numpy as np, json
from candidate_model import build_model
from dataset import load_split
cfg=json.load(open('task_config.json')); ck=torch.load('outputs/model.pt',weights_only=True)
m=build_model(cfg); m.load_state_dict(ck['state_dict']); m.eval()
fm,fs,tm,ts=ck['feat_mean'],ck['feat_std'],ck['targ_mean'],ck['targ_std']
def infer(p):
    x=(p-fm.view(1,1,-1))/fs.view(1,1,-1)
    with torch.no_grad(): o=m(x)
    return o*ts.view(1,-1)+tm.view(1,-1)
pt,_=load_split('data/public_test'); sel=pt[:40].clone()
vs=[50,60,70,75,80,85,90,95]; cd=[];cl=[]
for v in vs:
    s=sel.clone(); s[:,:,2]=v; pr=infer(s); cd.append(pr[:,0].median().item()); cl.append(pr[:,1].median().item())
print('V  :',' '.join(f'{v:7d}' for v in vs))
print('C_D:',' '.join(f'{c:7.5f}' for c in cd),'mono-dec:',all(cd[i]>cd[i+1] for i in range(len(cd)-1)))
print('C_L:',' '.join(f'{c:7.4f}' for c in cl),'(flat across V => vel-invariant)')
"

output

[verify] state_dict loaded strict=True: <All keys matched successfully>
[verify] public_test n=100 V range 75.0-81.6
[verify] pred C_D: min=0.00714 max=0.02876 mean=0.01187
[verify] pred C_L: min=-0.4963 max=1.6966 mean=0.6713
[verify] val nrmse_std: cd=0.0612 cl=0.0220 mean=0.0416
[verify] OK - checkpoint satisfies the inference contract.
=== velocity response ===
V  :      50      60      70      75      80      85      90      95
C_D: 0.01060 0.01023 0.00991 0.00973 0.00962 0.00953 0.00941 0.00936 mono-dec: True
C_L:  0.7696  0.7573  0.7554  0.7579  0.7637  0.7741  0.7888  0.8062 (flat across V => vel-invariant)

The final model (seed 13): - Passes the contract (strict load, finite, physical). ✓ - C_D velocity response monotonic decreasing from V=50 to V=95. ✓ (correct physics, no upturn) - C_L nearly flat across velocity (0.755-0.806 over V=50-95, slight drift at high V but small , ~4% over the test range 75-82, 0.758→0.764). Very stable. ✓ The val nrmse (cd 0.061, cl 0.022, mean 0.042) , optimistic since val is in training, but good. C_L flatness: over the test range V=75-82, C_L goes 0.758→~0.764, about 0.8% drift. Negligible. Good. Now let me finalize. Let me clean up the outputs directory (remove the backup and candidate files so only model.pt remains, or keep backup? The task only requires model.pt. Extra files shouldn't hurt, but let me keep the directory clean with just model.pt to be safe, and maybe keep the backup removed). Actually, let me remove the backup to avoid confusion , the grader reads model.pt. Let me keep only model.pt. Wait, let me reconsider whether to keep a backup. The task says write checkpoint to /app/outputs/model.pt. Extra files are harmless. But to be clean, I'll remove the backup copy. Actually, leaving model_backup there is harmless and provides a fallback. But it could be confusing. I'll remove it since model.pt is the deliverable and it's solid. Let me also double-check: the checkpoint tensors are plain CPU float32 tensors (not requiring grad), and the state_dict keys match. Let me verify dtypes and that there are no NaNs in weights. Let me also reconsider the overall solution quality and whether I've covered the requirement to do well on BOTH targets. - C_L: nrmse ~0.033 on held-out extrapolation, very stable. Excellent. - C_D: nrmse ~0.15 on held-out extrapolation (the harder target), cd_nmae ~2.6%. Good given shape-dependent drag is inherently hard. Both are well-handled, with C_D being the inherently harder one but still strong (and much better than physics-only 0.40, and better than baseline which additionally fails on C_L). Let me do final cleanup and a final listing + checkpoint integrity check. Actually, let me reconsider one potential issue: the grader might run the model on the hidden set which could have velocities beyond public_test's 81.6, maybe up to ~85. My model is monotonic and well-behaved out to 95 (augmented to 92), so that's covered. Also, could the hidden set have velocities LOWER than training (unlikely, it's "higher-speed")? No, it's the high end. Covered. Let me also make sure I'm not accidentally shipping a model that's overfit to the consensus in a bad way. The consensus selection just picks the most central of 5 good models , all 5 were trained identically with different seeds, all monotonic, all similar aggregate predictions. Seed 13 is representative. Good. Let me finalize: verify weights integrity, clean up, and write a short summary. Let me remove candidate/backup files. Actually, I want to double check there are no stray candidate files written by select_final.py , it didn't save candidates to disk (only kept in memory), so outputs/ has model.pt and model_backup. Let me check.

The final model (seed 13) is excellent: it passes the contract, its C_D response is monotonic-decreasing from V=50→95 (physically correct with no extrapolation upturn), and its C_L is essentially velocity-invariant (≈0.8% drift across the 75–82 test range). Let me verify the checkpoint's internal integrity and tidy the outputs directory.

bash

cd /app && python -c "
import torch
ck=torch.load('outputs/model.pt',weights_only=True)
print('keys:', list(ck.keys()))
nan=0; tot=0
for k,v in ck['state_dict'].items():
    tot+=v.numel(); nan+=torch.isnan(v).sum().item()+torch.isinf(v).sum().item()
print(f'state_dict tensors={len(ck[\"state_dict\"])} params={tot} nan/inf={nan}')
for k in ['feat_mean','feat_std','targ_mean','targ_std']:
    v=ck[k]; print(f'  {k}: shape={tuple(v.shape)} dtype={v.dtype} vals={[round(x,5) for x in v.tolist()]}')
" && echo "--- cleanup ---" && rm -f outputs/model_backup_seed20260518.pt && ls -la outputs/

output

keys: ['state_dict', 'feat_mean', 'feat_std', 'targ_mean', 'targ_std']
state_dict tensors=27 params=84741 nan/inf=0
  feat_mean: shape=(4,) dtype=torch.float32 vals=[0.38365, 0.0079, 58.78354, 0.07678]
  feat_std: shape=(4,) dtype=torch.float32 vals=[0.3956, 0.03333, 15.60666, 0.09548]
  targ_mean: shape=(2,) dtype=torch.float32 vals=[0.0125, 0.66186]
  targ_std: shape=(2,) dtype=torch.float32 vals=[0.005, 0.57806]
--- cleanup ---
total 347
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 19 23:54 .
drwxrws--- 5 h2tagent h2tagent   4096 Jun 19 23:38 ..
-rw-r--r-- 1 h2tagent h2tagent 346611 Jun 19 23:51 model.pt

The checkpoint is clean: - All 5 required keys present. - 27 state_dict tensors, 84741 params, 0 NaN/Inf. - All 4 normalization tensors present with correct shapes (4,), (4,), (2,), (2,) and float32. - feat_mean/std for velocity: mean 58.78, std 15.6 (reflects augmented range). - outputs/ contains only model.pt. Everything is in order. Let me do a final end-to-end verification run to be 100% sure the contract works, and summarize. Actually I already ran verify_checkpoint.py successfully. The solution is complete. Let me write a brief summary of what I did. But first, let me reconsider if there's any improvement I'm leaving on the table given remaining budget. Potential further improvements: 1. The C_D is the weaker target. Could I improve it? The shape-dependent drag residual is the hard part. The PointNet extracts it. More capacity isn't allowed (fixed arch). Better training (done: SWA, augmentation, more data). I think it's near the practical ceiling for this architecture. 2. I could reconsider the target normalization for C_D. Currently standardize by mean/std. Since C_D is right-skewed, the high-C_D cases (high aoa) dominate absolute error. The nrmse metric is absolute-error based. So this is aligned. No change needed. 3. Could I improve C_L further? It's already ~0.022-0.033. Very good. The residual is shape-dependent (camber). Near ceiling. I'm confident the solution is strong and robust. Let me finalize. Let me verify once more that the full inference path works by running verify_checkpoint one final time (it reads model.pt fresh). Already done above with good results. Let me also confirm the model file is readable with weights_only=True (the grader uses that). verify_checkpoint uses weights_only=True and it worked. The solution is complete. Let me write a concise summary for the user. Let me also make sure I restore/keep the key scripts (train_final.py, lib.py, select_final.py, verify_checkpoint.py) in place , they're in /app. They don't interfere with grading (grader reads model.pt). Good. Let me produce a final summary. Actually, let me reconsider whether I should present a final held-out estimate of performance. I have: - Held-out V≥69 (faithful proxy for the real test): mean_nrmse_std ≈ 0.09-0.10, cd ≈ 0.15, cl ≈ 0.033; cd_nmae ≈ 2.6%, nrmse_rng cd ≈ 0.037, cl ≈ 0.009. These are good numbers. Let me present them. One more sanity thing: let me make sure the public_test predictions look reasonable per-case (not degenerate). I saw C_D range [0.0071, 0.029], C_L range [-0.50, 1.70] , these span physical ranges matching training. Good. I'm done. Let me write the summary. Let me do a final confirmation listing of deliverable and a crisp recap.

Everything checks out. The final deliverable at `/app/outputs/model.pt` is clean: all 5 required keys, 27 weight tensors (84,741 params) with zero NaN/Inf, and all four normalization tensors present with correct shapes and dtypes. Let me run the verification one final time to confirm the end-to-end inference contract on the freshly-saved file.

bash

cd /app && python verify_checkpoint.py

output

[verify] state_dict loaded strict=True: <All keys matched successfully>
[verify] public_test n=100 V range 75.0-81.6
[verify] pred C_D: min=0.00714 max=0.02876 mean=0.01187
[verify] pred C_L: min=-0.4963 max=1.6966 mean=0.6713
[verify] val nrmse_std: cd=0.0612 cl=0.0220 mean=0.0416
[verify] OK - checkpoint satisfies the inference contract.

The solution is complete and verified. Here's a summary of what I did and why. ## Outcome The trained checkpoint is written to `/app/outputs/model.pt` with all five required entries (`state_dict` + the four normalization tensors), loads `strict=True` into the pinned PointNet, and produces finite, physically plausible `[C_D, C_L]` on the high-velocity public test cases. ## Key insight: the task is a velocity-extrapolation problem with a clean physical law The scored cases sit at inlet velocities (75–82 m/s) entirely **above** the training range (31–75 m/s). I analyzed the data and found: - **C_D ∝ V^(−0.21)** (R²=0.82, almost exactly the textbook turbulent skin-friction scaling V^(−0.2)), and this law extrapolates reliably , a fit on low-velocity cases predicts held-out high-velocity C_D with R²≈0.85. - **C_L is velocity-independent** (velocity coefficient −0.018 vs. aoa coefficient 5.68); it's driven by angle of attack plus shape-dependent camber. Critically, I showed a plain PointNet **extrapolates the wrong way**: beyond the training range its C_D turns *upward* (should decrease) and its C_L spuriously drifts up. ## What I changed in training (architecture untouched) 1. **Physics-informed velocity augmentation** , the core fix. Each epoch I rescale every case's inlet velocity across [35, 92] m/s, scaling C_D by V^(−0.21) and holding C_L fixed. This turns extrapolation into interpolation: it teaches the correct decreasing-C_D trend and C_L's velocity-invariance. 2. **Down-weighted C_D loss on augmented rows (0.4)** , real cases anchor the absolute C_D level while augmented rows still supply the (consistent) trend. I verified the learned C_D response still tracks V^(−0.21) out to V=95. 3. **SWA over a long training tail** , improved accuracy *and* cut the C_D seed variance roughly in half. 4. **Trained on train+val** for maximum airfoil-shape diversity; normalization fit over the augmented pool so test velocities map to ~+1.5σ (in-distribution). 5. **Consensus model selection** , since only one checkpoint ships, I trained 5 seeds and selected the one closest to the cross-seed consensus (all were monotonic in C_D), avoiding an outlier draw. I deliberately rejected changes that hurt: geometry jitter (destroys the shape→drag signal), heavier augmentation ratios, and per-target loss up-weighting. ## Validation Using a *faithful* pseudo-extrapolation split (train V<69, test V≥69 , the same ~9% step beyond the training max as the real test), the approach improves mean NRMSE ~25% over the baseline, with C_L NRMSE dropping from ~0.10 to ~0.033 and C_D held/improved. On the robust metrics the final model scores about **cd_nmae ≈ 2.6%** and range-normalized NRMSE of ≈0.037 (C_D) and ≈0.009 (C_L). The shipped model's C_D is monotonic-decreasing from V=50→95 and its C_L is flat (~0.8% drift across the test band), exactly as the physics requires. One caveat worth flagging: C_D remains the harder target (shape-dependent profile drag is inherently noisy for a max-pool PointNet), so its absolute error is larger than C_L's , but it's far better than a physics-only model (0.40 NRMSE) and well-behaved under extrapolation, which was the main risk in this task. The helper scripts I wrote (`lib.py`, `train_final.py`, `select_final.py`, `verify_checkpoint.py`) remain in `/app` for reproducibility; only `/app/outputs/model.pt` is read for grading.

Final diff , what the agent changed

No file changes, the agent left the workspace untouched.

Verifier output , the proof behind the reward

tests/test.sh

# SyncVals normalized verifier summary
# task: airfrans-high-reynolds-drag-extrapolation
# attempt: 8  status: completed  raw_score: 0.5
# reward rule: continuous: score 0.5 >= 0.5
# => reward = 1.0  (PASS)
#
# Note: these tasks score via an external hidden grader; no pytest/verifier
# block is embedded in the agent transcript. The block below is the last
# sizable tool-output captured in the run (supporting evidence).
----------------------------------------------------------------------
[verify] state_dict loaded strict=True: <All keys matched successfully>
[verify] public_test n=100 V range 75.0-81.6
[verify] pred C_D: min=0.00714 max=0.02876 mean=0.01187
[verify] pred C_L: min=-0.4963 max=1.6966 mean=0.6713
[verify] val nrmse_std: cd=0.0612 cl=0.0220 mean=0.0416
[verify] OK - checkpoint satisfies the inference contract.

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_0f94d593db944c63. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_0f94d593db944c63 · verifier authoritative; classifier explanatory.