SyncVals · Trajectory

airfrans-high-reynolds-drag-extrapolation

claude-code claude-opus-4-8 ✗ failed GOOD_FAILURE ↑ 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_FAILUREHonest miss, the agent ran correctly but couldn't solve it. Expected for a hard task; the task is sound.

SubtypeComplexity Overwhelm

EvidenceThe agent scored 0.08 on the hidden test, below the target of 0.076 (baseline 0.234, passing threshold 0.195). The agent demonstrated strong understanding of the extrapolation problem (training data velocity range 31-75 m/s, test range 75-82 m/s) and explored multiple sophisticated training strategies including velocity augmentation, loss functions, and hardware configuration. The agent created `/app/outputs/model.pt` successfully (346KB checkpoint). However, the agent ran out of time or failed to converge on an optimal configuration. The golden solution uses specialized techniques (velocity extrapolation augmentation with chord-mirror parity, EMA weight averaging, drag-weighted Huber loss with specific coefficients) that require deep aerodynamics knowledge and extensive hyperparameter tuning.

Root causeThis is a hard ML optimization task requiring specialized physics domain knowledge and extensive hyperparameter experimentation. The agent understood the problem well but couldn't find the optimal training recipe within the available time/compute budget. The target metric of 0.076 mean_nrmse requires sophisticated extrapolation-aware augmentation and loss weighting strategies that go well beyond standard deep learning practices.

RecommendationN/A - task is fine. This is a legitimately hard benchmark task designed to require domain expertise and careful experimentation. A 0.08 score is a reasonable failure point for an agent without aerodynamics domain knowledge. The task instructions are clear, tests are well-designed, and the problem is solvable (oracle passes with 0.076 target). The agent's failure is due to the inherent difficulty of finding optimal hyperparameters for velocity extrapolation in aerodynamic regression.

Trajectory

Tool-by-tool agent trajectory

209 tool calls · 3 tool types · 209 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 training code.

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

bash

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

output

===MODEL===
"""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===
"""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===
"""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, per-target loss bal
…[truncated]

Now let me understand the data. Let me load the splits and inspect them.

Now let me inspect the actual data to understand distributions, especially the velocity extrapolation aspect.

bash

cd /app && python3 -c "
import torch
for split in ['train','val','public_test']:
    p = f'data/{split}/split.pt'
    d = torch.load(p, weights_only=True)
    print('===', split, '===')
    print('keys:', list(d.keys()))
    pts = d['points']
    print('points shape/dtype:', pts.shape, pts.dtype)
    if 'targets' in d:
        t = d['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 && python3 -c "
import torch
def stats(name, pts, t=None):
    print('===', name, '===')
    # per-channel stats over all points
    flat = pts.reshape(-1,4)
    for i,ch in enumerate(['x','y','vel','aoa']):
        c = flat[:,i]
        print(f'  {ch}: min={c.min():.4f} max={c.max():.4f} mean={c.mean():.4f} std={c.std():.4f}')
    # velocity and aoa are per-case constant; get per-case values
    vel = pts[:,0,2]
    aoa = pts[:,0,3]
    print(f'  per-case vel: min={vel.min():.3f} max={vel.max():.3f} mean={vel.mean():.3f}')
    print(f'  per-case aoa: min={aoa.min():.4f} max={aoa.max():.4f} mean={aoa.mean():.4f}')
    if t is not None:
        for i,nm in enumerate(['C_D','C_L']):
            c=t[:,i]
            print(f'  {nm}: min={c.min():.5f} max={c.max():.5f} mean={c.mean():.5f} std={c.std():.5f}')
for split in ['train','val','public_test']:
    d = torch.load(f'data/{split}/split.pt', weights_only=True)
    stats(split, d['points'], d.get('targets'))
"

output

=== train ===
  x: min=-0.0070 max=1.0000 mean=0.3835 std=0.3956
  y: min=-0.0997 max=0.1627 mean=0.0079 std=0.0335
  vel: min=31.2830 max=74.9240 mean=52.9737 std=12.7859
  aoa: min=-0.0862 max=0.2565 mean=0.0764 std=0.0956
  per-case vel: min=31.283 max=74.924 mean=52.974
  per-case aoa: min=-0.0862 max=0.2565 mean=0.0764
  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 ===
  x: min=-0.0033 max=1.0000 mean=0.3846 std=0.3959
  y: min=-0.0939 max=0.1366 mean=0.0078 std=0.0323
  vel: min=31.4680 max=74.1620 mean=54.9773 std=11.3969
  aoa: min=-0.0836 max=0.2512 mean=0.0799 std=0.0947
  per-case vel: min=31.468 max=74.162 mean=54.977
  per-case aoa: min=-0.0836 max=0.2512 mean=0.0799
  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
=== public_test ===
  x: min=-0.0055 max=1.0000 mean=0.3761 std=0.3946
  y: min=-0.0961 max=0.1528 mean=0.0088 std=0.0346
  vel: min=75.0300 max=81.5930 mean=78.2086 std=1.9658
  aoa: min=-0.0857 max=0.2582 mean=0.0757 std=0.0961
  per-case vel: min=75.030 max=81.593 mean=78.209
  per-case aoa: min=-0.0857 max=0.2582 mean=0.0757

This is very informative. Key observations: 1. **Train velocity range: 31.28 - 74.92 m/s**, mean ~53. 2. **Val velocity range: 31.47 - 74.16 m/s**, similar to train. 3. **Public test velocity range: 75.03 - 81.59 m/s** , this is ENTIRELY ABOVE the training range! The test cases are at higher velocities. This confirms the extrapolation challenge: the scored (hidden) cases are sampled from the "higher-speed, higher-Reynolds end". The public_test is at 75-81.6 m/s, above the train max of 74.9. The hidden test is likely similar or even higher. AoA ranges are the same across all splits (-0.086 to 0.258 rad). The x,y geometry ranges are the same. So the key challenge: **the model must extrapolate to higher inlet velocities** (75-82+ m/s vs train 31-75). Now, the crucial physics insight: In aerodynamics, the force coefficients C_D and C_L are **dimensionless** and are *designed* to be largely independent of the flow velocity (they depend on Reynolds number and Mach number, but only weakly, and on the airfoil shape and angle of attack strongly). The lift coefficient C_L is primarily a function of angle of attack and airfoil shape. The drag coefficient C_D depends on shape, AoA, and Reynolds number (which scales with velocity). Wait , so actually the coefficients are normalized by dynamic pressure (0.5 * rho * V^2). So C_L and C_D should be *roughly* invariant to velocity, with only a weak Reynolds-number dependence. This is a HUGE insight. If C_L and C_D were purely functions of (shape, AoA) and independent of velocity, then the velocity channel would be irrelevant and there would be no extrapolation problem. But there IS a weak dependence (Reynolds effects, especially on drag). Let me check the correlation between velocity and the targets in the training data. Let me analyze this more. Let me think about the relationships: - C_L vs AoA: should be strongly linear (lift curve slope ~2*pi per radian for thin airfoils). - C_D vs AoA: should be roughly parabolic (drag polar: C_D = C_D0 + k*C_L^2). - C_D vs velocity (Reynolds): drag coefficient typically decreases with increasing Reynolds number. The danger with a naive neural net: if the network learns to use the velocity channel in a way that fits the training range 31-75, it may extrapolate poorly to 75-82. Key strategy ideas: 1. **Understand the physics**: C_L and C_D normalized coefficients. Let me check how much velocity actually matters in training data. 2. **Normalization for extrapolation**: The standardization of the velocity channel matters. With train mean ~53, std ~12.8, a test velocity of 81.6 maps to (81.6-53)/12.8 = +2.2 std. That's within a reasonable range but beyond training's max of +1.7 std. The network behavior beyond the training range is what matters. Let me investigate the actual dependence of targets on velocity and AoA. Let me do some correlation/regression analysis. Let me think carefully about how to model this. Since the key risk is extrapolation in velocity, I want to understand the true functional dependence. Let me do a careful data analysis. I'll check: - For similar airfoil shapes at different velocities, how do C_D and C_L change? Actually, since we don't have a shape ID, let me instead fit a simple model: regress C_L and C_D on AoA and velocity features, and look at residuals / coefficients. Actually, let me first understand Reynolds number physics. AirFRANS dataset: This is a known dataset (AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions). The inlet velocities range from ~30 to ~80 m/s (Mach ~0.09 to 0.23), and the angles of attack from -5 to +15 degrees. Reynolds numbers from 2 to 6 million based on chord (chord=1m, nu=1.56e-5 for air). Actually in AirFRANS, the setup: Reynolds between 2 and 6 million, varying by inlet velocity (since chord=1, nu fixed). U between ~31 and ~93 m/s? Let me not rely on memory. The key: C_L and C_D. For these airfoils at these Rey …[truncated]

This reveals the core challenge precisely. Let me quantify it: - **Train velocity:** 31.3–74.9 m/s (mean 53) - **Val velocity:** 31.5–74.2 m/s (basically same as train) - **Public test velocity:** 75.0–81.6 m/s , **entirely above the training range** So the scored cases live at higher inlet velocities than anything in training. The angle-of-attack and geometry ranges are unchanged. This is a pure velocity-extrapolation problem. Let me investigate the physics: force coefficients are normally normalized by dynamic pressure, so C_L/C_D should be *weakly* dependent on velocity (only through Reynolds number). Let me verify how much velocity actually drives the targets.

bash

cd /app && python3 -c "
import torch, numpy as np
d = torch.load('data/train/split.pt', weights_only=True)
pts, t = d['points'], d['targets']
vel = pts[:,0,2].numpy(); aoa = pts[:,0,3].numpy()
cd = t[:,0].numpy(); cl = t[:,1].numpy()
import numpy as np
print('corr(vel, C_D):', np.corrcoef(vel, cd)[0,1])
print('corr(vel, C_L):', np.corrcoef(vel, cl)[0,1])
print('corr(aoa, C_D):', np.corrcoef(aoa, cd)[0,1])
print('corr(aoa, C_L):', np.corrcoef(aoa, cl)[0,1])
# Multi-linear regression C ~ aoa, aoa^2, vel
X = np.stack([np.ones_like(aoa), aoa, aoa**2, vel], axis=1)
for nm, y in [('C_D', cd), ('C_L', cl)]:
    coef, res, *_ = np.linalg.lstsq(X, y, rcond=None)
    pred = X@coef
    ss = 1 - ((y-pred)**2).sum()/((y-y.mean())**2).sum()
    print(f'{nm}: coefs(1,aoa,aoa^2,vel)=', np.round(coef,6), 'R2=', round(ss,4))
"

output

corr(vel, C_D): -0.15417731232687426
corr(vel, C_L): -0.022042552660221815
corr(aoa, C_D): 0.738708826123821
corr(aoa, C_L): 0.9433278860776938
C_D: coefs(1,aoa,aoa^2,vel)= [ 1.22060e-02 -4.53900e-03  2.65212e-01 -5.80000e-05] R2= 0.7616
C_L: coefs(1,aoa,aoa^2,vel)= [ 2.508290e-01  6.184932e+00 -3.041384e+00 -3.430000e-04] R2= 0.8919

Very informative: - **C_L** is dominated by AoA (correlation 0.943). Lift curve slope ~6.18 per radian (close to 2π≈6.28, makes physical sense!). Velocity has essentially no effect (coef -0.00034, correlation -0.022). C_L is nearly velocity-independent. - **C_D** is dominated by AoA (0.739 correlation), with parabolic dependence. Velocity has a small negative effect (-0.0000058 per m/s, correlation -0.154) , consistent with drag coefficient decreasing with Reynolds number. So the physics confirms: C_L and C_D are mostly functions of (shape, AoA), with only weak velocity (Reynolds) dependence. The weak velocity dependence is negative for drag (higher Re → lower C_D). The R² for the simple model is 0.76 (C_D) and 0.89 (C_L), so shape matters too (the point cloud geometry captures shape). The remaining variance is from airfoil shape, which the PointNet reads from the x,y geometry. The extrapolation concern: since velocity has a weak effect, the main risk is that a neural net OVERFITS to the velocity channel, learning spurious correlations that blow up when extrapolating to 75-82 m/s. Key strategy: I want the network to rely primarily on geometry + AoA, and use velocity only for its mild physical effect. Ideas: 1. **Reduce/control the velocity channel's leverage during extrapolation.** Options: - Feature engineering: but I can't change the architecture or input dim (must be 4 channels: x, y, vel, aoa). The checkpoint feeds [x,y,vel,aoa] standardized by feat_mean/feat_std. Actually , wait. Can I transform the velocity channel via the feat_mean/feat_std? The standardization is linear: (v - mean)/std. I could choose feat_mean/feat_std to control the scale. But the input channels themselves are fixed as given (x, y, inlet_velocity, aoa). The grader reads points, applies (points - feat_mean)/feat_std, then forward. So I can only apply an affine transform per channel via mean/std. I cannot apply a nonlinear transform to velocity. Hmm, but actually , do I *have* to use the raw velocity? The feat_mean and feat_std are provided by me. If I set feat_std for the velocity channel to be very large, the velocity input becomes tiny (near constant), effectively suppressing the velocity channel. But that would also prevent learning the (beneficial) weak Reynolds effect. Given the weak effect, suppressing might reduce variance but the effect is real and negative for drag... Actually, a cleaner idea: Because C_L and C_D are *designed* to be dimensionless and roughly velocity-independent, the safest extrapolation is to make the model as velocity-insensitive as possible, OR to ensure the velocity dependence learned is physically monotonic and mild so it extrapolates gracefully. 2. **Training-time augmentation on velocity:** I can augment the velocity channel during training , e.g., randomly perturb/scale velocities , to teach the network that velocity shouldn't change the prediction much, and to cover the higher range. But wait, if I change velocity I'd need to change the target accordingly. Since C is nearly velocity-independent, I could augment velocity while keeping targets fixed (data augmentation that says "the coefficient is invariant to velocity within reason"). This directly encodes the physics prior and forces the network to be robust/flat in velocity, which is exactly what helps extrapolation to 75-82 m/s. This is a strong idea. But it would wipe out the weak beneficial Re effect on C_D. Given C_D's velocity coef is tiny and the benefit of robust extrapolation is large, maybe partial augmentation (small jitter) is best , jitter velocity by a moderate amount so the net learns local flatness but can still capture a gentle trend. Actually, better: I can augment velocity to EXTEND the range up to and beyond test (e.g., sample velocities up to 85-90), keeping targets fixed (invariance assumption) OR applying the mild learned Reynolds correction. Since the true effect is weak, keeping targets fixed while extending velocit …[truncated]

The physics is now clear and very favorable to reason about: - **C_L** is almost entirely determined by angle of attack (corr 0.943, slope ≈6.18/rad ≈ 2π, the classic thin-airfoil result) plus shape. Velocity has negligible effect (corr −0.02). - **C_D** is driven by AoA (parabolic drag polar) plus shape, with only a **weak negative velocity dependence** (corr −0.15), consistent with drag coefficient dropping slowly as Reynolds number rises. So the coefficients are nearly velocity-invariant (as dimensionless coefficients should be). The big extrapolation risk is a network **overusing the velocity channel** and diverging above 75 m/s. Let me check hardware and build a proper extrapolation-validation harness, since my given val set is in-range and can't measure extrapolation.

bash

cd /app && python3 -c "
import torch
print('cuda:', torch.cuda.is_available())
print('torch:', torch.__version__)
import os; print('cpus:', os.cpu_count())
" && echo "---" && free -h 2>/dev/null | head -2; nvidia-smi -L 2>/dev/null || echo "no gpu"

output

cuda: False
torch: 2.5.1+cu124
cpus: 16
---
               total        used        free      shared  buff/cache   available
Mem:            69Gi        68Mi        68Gi          0B        29Mi        68Gi
no gpu

CPU only, 16 cores, 69 GB RAM. The dataset is tiny (630 train cases, 256 points each). Training will be fast on CPU. Let me set threads appropriately. Let me build the extrapolation validation harness. I'll create a module that: 1. Loads train data. 2. Splits by velocity: train on low-velocity, validate on high-velocity (the held-out top). 3. Trains a model with various options. 4. Evaluates NRMSE on the high-vel holdout. Let me define the plan for experiments in a single script with configurable options. Let me first look at the distribution of velocities in train to pick split thresholds.

CPU-only but the dataset is tiny (630 cases), so training will be fast. Let me look at the velocity distribution in training to design a realistic extrapolation holdout.

bash

cd /app && python3 -c "
import torch, numpy as np
d = torch.load('data/train/split.pt', weights_only=True)
vel = d['points'][:,0,2].numpy()
print('vel percentiles:')
for p in [0,10,25,50,75,90,95,100]:
    print(f'  {p}%: {np.percentile(vel,p):.2f}')
print('counts by band:')
for lo,hi in [(31,50),(50,60),(60,65),(65,70),(70,75)]:
    print(f'  [{lo},{hi}): {((vel>=lo)&(vel<hi)).sum()}')
print('n with vel>=65:', (vel>=65).sum())
print('n with vel>=68:', (vel>=68).sum())
print('n with vel>=70:', (vel>=70).sum())
"

output

vel percentiles:
  0%: 31.28
  10%: 35.42
  25%: 41.74
  50%: 53.27
  75%: 64.14
  90%: 70.35
  95%: 72.63
  100%: 74.92
counts by band:
  [31,50): 272
  [50,60): 141
  [60,65): 69
  [65,70): 78
  [70,75): 70
n with vel>=65: 148
n with vel>=68: 95
n with vel>=70: 70

Good. Velocities are fairly uniformly spread 31-75. For an extrapolation proxy: - Train on vel < 65 (482 cases), validate on vel >= 65 (148 cases). The holdout is 65-75, and the model trains on 31-65. This simulates extrapolating ~0-10 m/s beyond the training max of 65. The real task extrapolates 75→82, i.e., training max 75, test up to 82 (~7 m/s beyond, up to ~10% beyond max). So holding out >=65 while training on <65 (test 65-75, up to 15% beyond max of 65) is a reasonable, slightly harder proxy. Good. Actually to mirror "test beyond max", note test max 81.6 vs train max 74.9 → about 9% beyond. My proxy: holdout max 74.9 vs subtrain max ~65 → 15% beyond. Slightly harder, which is a conservative/safe proxy. Let me also make a second proxy: train < 60, val >= 68 (with a gap), to really test extrapolation with a clean gap. I'll use both. Now let me write a comprehensive, reusable training+eval harness. I'll make it configurable via a dict of options: - loss: mse / huber - vel_aug: None, 'jitter' (multiplicative/additive), 'extend' (sample wide range), with target policy 'invariant' or 'trend' - vel_suppress: factor to inflate feat_std for velocity - standardization choices - epochs, lr, schedule, weight decay - augmentation: geometry jitter, point dropout/resampling, rotation? Wait , rotation augmentation for airfoils: the geometry x,y is chord-normalized. Rotating the point cloud would change the effective angle of attack. Actually AoA is given as a separate channel; rotating geometry without changing AoA channel would be inconsistent. Better NOT to rotate geometry. The starter text mentions rotation augmentation as a generic suggestion, but for this physics it's not obviously right because AoA is encoded separately from geometry. Actually, in AirFRANS the airfoil geometry is given in chord coordinates (not rotated by AoA); the AoA is a separate flow condition. So rotating the geometry would correspond to changing the airfoil shape orientation, which is not physical unless we also change AoA. I'll avoid rotation. Point jitter (small noise on x,y): could help regularize shape reading. Point resampling/permutation: PointNet with max-pool is permutation invariant, so permutation doesn't matter. Point subsampling/dropout: could help robustness but we have fixed 256 points at test. Mild jitter on geometry might help generalization across shapes. I'll test. The most important: velocity handling. Let me implement velocity augmentation where during training I replace the per-sample velocity with a random draw, keeping the target fixed (invariance prior), possibly with a small probability of keeping original. And also the option to apply a mild Reynolds trend correction to C_D. Let me think about the target policy for velocity augmentation more rigorously. If the true relationship is C_D(shape, aoa, V) ≈ f(shape, aoa) + g(V) where g is a weak decreasing function, then: - For a training sample with (shape_i, aoa_i, V_i, C_D_i), if I change V_i → V', the "correct" target would be C_D_i + (g(V') - g(V_i)). - If g is weak/negligible, C_D_i is approximately the target for any V'. So invariance is approximately correct. The estimated slope from the linear fit was dC_D/dV ≈ -5.8e-6 per m/s. Over a 10 m/s change, that's -5.8e-5, tiny relative to C_D std 5.1e-3 (about 1%). So invariance introduces ~1% error , negligible. For C_L, slope -3.4e-4 per m/s, over 10 m/s = -3.4e-3, vs C_L std 0.576 , totally negligible. Wait, but these slopes are confounded with shape/AoA correlations across the dataset (cases at different velocities also have different shapes/AoA). The *causal* velocity effect (same shape & AoA, vary V) might differ. Without shape IDs, hard to isolate. But physically the causal effect is the Reynolds effect, which is known to be weak and in the same direction (C_D decreases with Re). Given all this, a **velocity-invariance augmentation** is well-justified: it encodes the correct physics prior (coefficients ~ velocity-in …[truncated]

Velocities span 31–75 fairly uniformly. For an extrapolation proxy I'll train on the lower band and validate on the top band. First, let me check whether the same airfoil geometry appears at multiple velocities , that would let me isolate the *causal* velocity (Reynolds) effect on the coefficients, cleanly separating it from shape/AoA confounds.

bash

cd /app && python3 -c "
import torch, numpy as np
d = torch.load('data/train/split.pt', weights_only=True)
pts, t = d['points'], d['targets']
geo = pts[:,:,:2].reshape(pts.shape[0],-1).numpy()  # x,y only
# round to reduce float noise
key = np.round(geo,4)
import hashlib
hashes = [hashlib.md5(k.tobytes()).hexdigest() for k in key]
from collections import defaultdict
groups = defaultdict(list)
for i,h in enumerate(hashes): groups[h].append(i)
sizes = [len(v) for v in groups.values()]
print('n unique geometries:', len(groups), 'of', len(hashes))
print('group size distribution:', {s:sizes.count(s) for s in sorted(set(sizes))})
# Show a few multi-velocity groups
multi = [v for v in groups.values() if len(v)>=3][:5]
vel = pts[:,0,2].numpy(); aoa = pts[:,0,3].numpy(); cd=t[:,0].numpy(); cl=t[:,1].numpy()
for g in multi:
    g=sorted(g, key=lambda i: vel[i])
    print('--- group ---')
    for i in g:
        print(f'  vel={vel[i]:.2f} aoa={aoa[i]:.4f} C_D={cd[i]:.5f} C_L={cl[i]:.4f}')
"

output

n unique geometries: 630 of 630
group size distribution: {1: 630}

All 630 geometries are unique , each case is a distinct (shape, aoa, velocity) combination. So I can't directly isolate the velocity effect via repeated shapes. The AoA and shape vary with every case. In AirFRANS, each simulation has a unique shape (NACA 4/5-digit parameterized) sampled along with aoa and velocity. So shape is always different. OK so I'll rely on the physics prior + the extrapolation proxy to choose strategy. Let me now build a robust training/eval harness and run controlled experiments on the velocity-extrapolation proxy. Let me design the harness carefully: ```python def make_splits(mode): # returns train_idx, val_idx based on velocity ``` Experiments to run (on proxy: train vel<65, val vel>=65): 1. baseline: standardize, MSE, 40 epochs (like starter). 2. baseline + more epochs + cosine schedule. 3. + velocity invariance augmentation (jitter velocity widely, targets fixed). 4. velocity suppressed (std huge). 5. huber loss. 6. geometry jitter. Metric: per-target NRMSE = RMSE/std_true(val), mean and worst; plus cd mae. Let me also always compare against a trivial baseline: predict mean of training targets → NRMSE = std_val/std_train ≈ 1 if distributions match. Actually NRMSE of predicting train-mean on val = sqrt(mean((y-ybar_train)^2))/std_val. If val dist == train dist, ≈1.0. So NRMSE < 1 means better than mean prediction. Also a physics linear baseline: fit C ~ (1, aoa, aoa^2) on subtrain (ignoring velocity, so it extrapolates trivially), predict on val. This tells me how much the geometry (beyond aoa) helps. The PointNet should beat this by reading shape. Let me write the harness now. I'll make training deterministic-ish and fast. For speed on CPU: 630 cases, batch 32, model is small. Each epoch ~20 batches. Should be <1s/epoch maybe. Let me set torch threads to 16. Let me write a module `harness.py`. Let me think about the normalization of velocity for the "invariance" augmentation. During training, for a fraction of samples, I draw a random velocity from a wide range (say uniform[30, 95] or even wider) and set the velocity channel to that, keeping target fixed. This explicitly teaches flatness across the whole range including test (75-82). I should make sure feat_mean/feat_std for velocity are set so that the standardized values across [30,95] are reasonable (e.g., center ~ 60, std ~ 18). Actually I should compute feat_mean/feat_std from the augmented distribution or just from original train; it's a linear scaling so it doesn't fundamentally matter, but for BN health, I want the standardized velocity to be roughly unit-scale across the augmented range. If I augment uniformly over [30,95], mean ~62.5, std ~ 18.8. I'll set feat_std velocity to cover this. But the saved feat_mean/feat_std must match what I used in training. I'll just compute them from the original train points OR set them manually. Let me set them to cover the extended range so test values (75-82) standardize to modest magnitudes. E.g., feat_mean_vel = 60, feat_std_vel = 18. Then test 81.6 → +1.2. Good , within the augmented training range. Actually, simpler and safe: compute feat_mean/feat_std from a representative set that includes the augmentation range, so the numbers are consistent. I'll just hardcode velocity mean/std to reasonable values and use them both in training and saving. For x,y,aoa use train stats. Wait, aoa: test aoa range is same as train (-0.086 to 0.258). So no extrapolation in aoa. Use train stats. Good. Let me write the harness and run baseline first to establish numbers. Actually, I want to be careful about what "invariance augmentation" does to the learnable weak Reynolds effect. If I enforce strict invariance, I give up ~1% of C_D variance. But the alternative (letting the net model velocity) risks much larger extrapolation error. The proxy will tell me. Let me also try a "mild jitter" variant: jitter velocity by a moderate amount (e.g., ±15 m/s, or multiplicative ±25%), targets fixed. This tea …[truncated]

Each of the 630 cases is a unique geometry, so I can't isolate the pure Reynolds effect from repeated shapes , I'll rely on the physics prior plus an empirical extrapolation proxy. Let me build a reusable harness that holds out the **top velocity band** for validation (train on low velocities, validate on high) to actually measure extrapolation, and supports the strategies I want to compare.

bash

cd /app && cat > harness.py << 'PYEOF'
"""Experiment harness for the airfoil force-coefficient extrapolation task."""
from __future__ import annotations
import json, os, time, math
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

APP = Path('/app')
import sys; sys.path.insert(0, str(APP))
from candidate_model import build_model

torch.set_num_threads(16)
with (APP/'task_config.json').open() as fh:
    CFG = json.load(fh)

def load(split):
    d = torch.load(APP/'data'/split/'split.pt', weights_only=True)
    return d['points'].float(), (d['targets'].float() if 'targets' in d else None)

TRAIN_PTS, TRAIN_T = load('train')
VAL_PTS, VAL_T = load('val')

def vel_split(pts, thr_lo, thr_hi):
    """indices for subtrain (vel<thr_lo) and holdout (vel>=thr_hi)."""
    v = pts[:,0,2]
    return torch.where(v < thr_lo)[0], torch.where(v >= thr_hi)[0]

def nrmse(pred, true):
    # per-target RMSE normalized by std of true targets on this eval set
    rmse = torch.sqrt(((pred-true)**2).mean(0))
    std = true.std(0).clamp_min(1e-8)
    return (rmse/std)

def evaluate(model, pts, true, feat_mean, feat_std, targ_mean, targ_std):
    model.eval()
    with torch.no_grad():
        x = (pts - feat_mean.view(1,1,-1))/feat_std.view(1,1,-1)
        out = model(x)
        pred = out*targ_std.view(1,-1) + targ_mean.view(1,-1)
    n = nrmse(pred, true)
    mae = (pred-true).abs().mean(0)
    return {'nrmse_cd': n[0].item(), 'nrmse_cl': n[1].item(),
            'mean_nrmse': n.mean().item(), 'worst_nrmse': n.max().item(),
            'cd_mae': mae[0].item(), 'cl_mae': mae[1].item()}, pred

def train_model(train_pts, train_t, opts, seed=0):
    torch.manual_seed(seed); np.random.seed(seed)
    dev = 'cpu'
    # ---- feature normalization (velocity can be overridden for extended range)
    flat = train_pts.reshape(-1,4)
    fm = flat.mean(0).clone(); fs = flat.std(0).clamp_min(1e-8).clone()
    if opts.get('vel_meanstd'):
        fm[2], fs[2] = opts['vel_meanstd']
    if opts.get('vel_suppress'):
        fs[2] = fs[2]*opts['vel_suppress']
    tm = train_t.mean(0).clone(); ts = train_t.std(0).clamp_min(1e-8).clone()
    if opts.get('targ_log_cd'):
        pass
    model = build_model(CFG).to(dev)
    epochs = opts.get('epochs', 120)
    bs = opts.get('bs', 32)
    lr = opts.get('lr', 1e-3)
    wd = opts.get('wd', 1e-4)
    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=wd)
    sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=epochs)
    n = train_pts.shape[0]
    va = opts.get('vel_aug'); geo_j = opts.get('geo_jitter',0.0)
    loss_type = opts.get('loss','mse'); huber_d = opts.get('huber_delta',1.0)
    w = torch.tensor(opts.get('tw',[1.0,1.0]))
    for ep in range(epochs):
        model.train()
        perm = torch.randperm(n)
        for s in range(0,n,bs):
            idx = perm[s:s+bs]
            xb = train_pts[idx].clone(); yb = train_t[idx].clone()
            # velocity augmentation (invariance / extend)
            if va:
                b = xb.shape[0]
                if va['mode']=='extend':
                    p = va.get('p',1.0)
                    m = torch.rand(b) < p
                    newv = torch.empty(b).uniform_(va['lo'], va['hi'])
                    xb[m,:,2] = newv[m].view(-1,1)
                elif va['mode']=='jitter':
                    p = va.get('p',1.0)
                    m = (torch.rand(b) < p).float().view(-1,1)
                    fac = torch.empty(b,1).uniform_(1-va['rel'],1+va['rel'])
                    xb[:,:,2] = xb[:,:,2]*(1+m*(fac-1))
            if geo_j>0:
                xb[:,:,:2] = xb[:,:,:2] + torch.randn_like(xb[:,:,:2])*geo_j
            x = (xb - fm.view(1,1,-1))/fs.view(1,1,-1)
            y = (yb - tm.view(1,-1))/ts.view(1,-1)
            opt.zero_grad(set_to_none=True)
            out = model(x)
            if loss_type=='mse':
                loss = (w*( (out-y)**2 )).mean()
            else:
                
…[truncated]

output

harness written

Let me first run a baseline on the proxy to get numbers, and also establish the "predict mean" and "physics linear" baselines for reference.

Let me establish reference baselines on the extrapolation proxy (train on vel<65, validate on vel≥65): a trivial mean-predictor, a physics linear model, and the starter-style PointNet.

bash

cd /app && python3 -c "
import torch, numpy as np
from harness import TRAIN_PTS, TRAIN_T, vel_split, nrmse
sub,hold = vel_split(TRAIN_PTS,65,65)
tt=TRAIN_T[sub]; et=TRAIN_T[hold]
# trivial: predict train mean
pred = tt.mean(0,keepdim=True).repeat(et.shape[0],1)
n=nrmse(pred,et); print('MEAN-predictor    : mean_nrmse=%.4f cd=%.4f cl=%.4f'%(n.mean(),n[0],n[1]))
# physics linear on aoa (ignore velocity -> extrapolates trivially)
aoa_s=TRAIN_PTS[sub,0,3].numpy(); aoa_h=TRAIN_PTS[hold,0,3].numpy()
Xs=np.stack([np.ones_like(aoa_s),aoa_s,aoa_s**2],1); Xh=np.stack([np.ones_like(aoa_h),aoa_h,aoa_h**2],1)
pr=[]
for i in range(2):
    c,*_=np.linalg.lstsq(Xs,tt[:,i].numpy(),rcond=None); pr.append(Xh@c)
pr=torch.tensor(np.stack(pr,1),dtype=torch.float32)
n=nrmse(pr,et); print('PHYS-linear(aoa)  : mean_nrmse=%.4f cd=%.4f cl=%.4f'%(n.mean(),n[0],n[1]))
"

output

MEAN-predictor    : mean_nrmse=1.0186 cd=1.0387 cl=0.9985
PHYS-linear(aoa)  : mean_nrmse=0.4096 cd=0.5138 cl=0.3054

Good reference points on the proxy holdout (vel>=65): - MEAN predictor: mean_nrmse ~1.02 (as expected) - PHYS-linear on aoa only (velocity-independent → trivially extrapolates): mean_nrmse 0.41, cd 0.51, cl 0.31. So a simple aoa-only model that ignores velocity already gets 0.41. The PointNet should beat this by reading shape. Let me now run the PointNet experiments. Let me run the starter-style baseline (MSE, 40 epochs, plain Adam) and a longer version, then the velocity-handling variants. Let me run a batch of experiments. To save time, I'll run them in one script with seeds=(0,1) for a bit of stability. Let me first do single seed to iterate fast, then confirm best with multiple seeds.

Good references: the trivial mean-predictor gives mean_NRMSE ≈ 1.02, and an AoA-only physics model that *ignores velocity* (so it extrapolates trivially) already achieves 0.41. The PointNet must beat 0.41 by reading shape while not breaking on velocity. Let me run the core comparison of velocity-handling strategies on the proxy.

bash

cd /app && python3 -c "
from harness import run
import time
base = dict(epochs=120, bs=32, lr=1e-3, wd=1e-4, loss='mse')
print('A. starter-style (40ep, plain):')
run(dict(base, epochs=40))
print('B. longer 120ep cosine:')
run(base)
print('C. vel suppressed x8 (nearly ignore velocity):')
run(dict(base, vel_suppress=8.0))
print('D. vel invariance extend [30,95] p=0.7:')
run(dict(base, vel_aug={'mode':'extend','lo':30,'hi':95,'p':0.7}, vel_meanstd=(62.0,18.0)))
print('E. vel jitter rel=0.25 p=0.8:')
run(dict(base, vel_aug={'mode':'jitter','rel':0.25,'p':0.8}))
" 2>&1 | grep -v Warning

output

A. starter-style (40ep, plain):
  train_n=482 hold_n=148 | mean_nrmse=0.2079 cd=0.2364 cl=0.1793 worst=0.2364 cd_mae=0.00071
B. longer 120ep cosine:
  train_n=482 hold_n=148 | mean_nrmse=0.1473 cd=0.1841 cl=0.1105 worst=0.1841 cd_mae=0.00060
C. vel suppressed x8 (nearly ignore velocity):
  train_n=482 hold_n=148 | mean_nrmse=0.4611 cd=0.8351 cl=0.0871 worst=0.8351 cd_mae=0.00076
D. vel invariance extend [30,95] p=0.7:
  train_n=482 hold_n=148 | mean_nrmse=0.3482 cd=0.5927 cl=0.1037 worst=0.5927 cd_mae=0.00121
E. vel jitter rel=0.25 p=0.8:
  train_n=482 hold_n=148 | mean_nrmse=0.2390 cd=0.3595 cl=0.1184 worst=0.3595 cd_mae=0.00068

Interesting and somewhat counterintuitive results: - A (starter 40ep): mean 0.208 - B (120ep cosine): mean 0.147 , best so far! Longer training helps a lot. - C (vel suppressed x8): mean 0.461, cd 0.835 , MUCH worse on C_D! Suppressing velocity hurts drag badly. - D (vel invariance extend): mean 0.348, cd 0.593 , worse on cd. - E (vel jitter): mean 0.239, cd 0.360 , worse than B. Wait, this is surprising. The velocity-invariance / suppression approaches HURT, especially on C_D. This means velocity IS informative for C_D extrapolation, and suppressing it hurts. The plain model (B) that uses velocity normally extrapolates BETTER than the invariance approaches. Hmm, let me reconsider. Why would suppressing velocity hurt C_D extrapolation so much? Because on the holdout (vel 65-75), the C_D values are systematically affected by velocity (Reynolds). If I ignore velocity, I predict the average C_D for a given shape/aoa (averaged over training velocities 31-65, mean ~47), but the holdout is at higher velocity (65-75) where C_D is lower (negative trend). So ignoring velocity → overpredict C_D on holdout → higher error. Using velocity lets the model track the downward trend and extrapolate it. Wait but that means the model CAN extrapolate the velocity trend correctly (model B does well). The concern about catastrophic extrapolation isn't materializing in the proxy , the plain model extrapolates the mild velocity trend fine and benefits from it. So my physics reasoning was partially off: while the velocity effect is "weak" in correlation terms, it's systematic enough that on the holdout, ignoring it introduces a bias that hurts more than the extrapolation risk. But WAIT. There's a subtlety. My proxy holds out vel>=65 training on vel<65. The holdout is only slightly beyond the training range (training 31-65, holdout 65-75). The model interpolates/extrapolates the velocity trend over a modest range. The real test is vel 75-82, beyond the FULL training range (31-75). The question is whether the velocity trend the model learns continues to extrapolate well to 75-82. So my proxy might be too optimistic about the plain model because the holdout (65-75) is close to training (31-65). Let me make a harder proxy with a bigger gap and more extrapolation: train on vel<55, hold out vel>=68. This tests extrapolating further beyond training max. Also, I should test: does the plain model's velocity trend extrapolate linearly/smoothly, or does it do something weird beyond the training range? Let me probe the model's velocity response by feeding a fixed shape/aoa and sweeping velocity from 30 to 95. Let me do both: (1) harder proxy, (2) velocity-response probe. Let me run harder proxy first.

Surprising and important result: using velocity **normally** (B, longer training) extrapolates *best* (0.147), while suppressing or forcing invariance on velocity badly hurts **C_D** (C: cd_nrmse 0.84). So velocity carries a real, useful signal for drag, and the model tracks it. But my proxy holdout (65–75) sits just past the sub-train range (31–65); the true test reaches 75–82, further out. Let me stress-test with a harder gap and probe how the plain model behaves when velocity is swept *beyond* its training range.

bash

cd /app && python3 -c "
from harness import run
print('Harder proxy: train vel<55, holdout vel>=68 (gap, further extrapolation)')
base = dict(epochs=120, bs=32, lr=1e-3, wd=1e-4, loss='mse')
print('B plain:')
run(base, thr_lo=55, thr_hi=68)
print('E jitter .25:')
run(dict(base, vel_aug={'mode':'jitter','rel':0.25,'p':0.8}), thr_lo=55, thr_hi=68)
print('C suppress x8:')
run(dict(base, vel_suppress=8.0), thr_lo=55, thr_hi=68)
print('D extend invariance:')
run(dict(base, vel_aug={'mode':'extend','lo':30,'hi':95,'p':0.7}, vel_meanstd=(62.0,18.0)), thr_lo=55, thr_hi=68)
" 2>&1 | grep -v Warning

output

Harder proxy: train vel<55, holdout vel>=68 (gap, further extrapolation)
B plain:
  train_n=337 hold_n=95 | mean_nrmse=0.4505 cd=0.6330 cl=0.2680 worst=0.6330 cd_mae=0.00215
E jitter .25:
  train_n=337 hold_n=95 | mean_nrmse=0.2503 cd=0.3365 cl=0.1640 worst=0.3365 cd_mae=0.00106
C suppress x8:
  train_n=337 hold_n=95 | mean_nrmse=0.3064 cd=0.5346 cl=0.0783 worst=0.5346 cd_mae=0.00101
D extend invariance:
  train_n=337 hold_n=95 | mean_nrmse=0.2511 cd=0.4471 cl=0.0552 worst=0.4471 cd_mae=0.00141

Now with the harder proxy (train vel<55, holdout vel>=68, a real gap and further extrapolation), the story flips: - B plain: mean 0.451, cd 0.633 , BAD! The plain model extrapolates poorly when the gap is bigger. - E jitter .25: mean 0.250, cd 0.337 , much better! - C suppress x8: mean 0.306, cd 0.535 - D extend invariance: mean 0.251, cd 0.447, cl 0.055 So when the extrapolation gap is larger (training max 55, test 68-75, i.e., ~13-20 m/s beyond max), the plain model B degrades badly (cd 0.633), while jitter augmentation (E) is far more robust (cd 0.337). This confirms the extrapolation risk is real, and velocity jitter augmentation helps a lot. The real test is train max 75, test 75-82 (~0-7 beyond max, ~9% beyond). My two proxies bracket this: - Proxy 1 (train<65, test 65-75): test is 0-15% beyond sub-train max → plain B best (0.147). - Proxy 2 (train<55, test 68-75): test is 24-36% beyond sub-train max → jitter best. The real test is ~9% beyond max, which is closer to proxy 1's regime (0-15% beyond) but the actual distances... Let me quantify "fraction beyond max": - Real: train max 74.92, test up to 81.59 → 9% beyond, test min 75.03 → 0.1% beyond. - Proxy 1: subtrain max ~64.9 (vel<65), test 65-74.9 → 0% to 15% beyond. - Proxy 2: subtrain max ~54.x (vel<55), test 68-74.9 → ~24% to 37% beyond. So the real test (0-9% beyond max) is MOST similar to proxy 1 (0-15% beyond). In proxy 1, plain B wins (0.147). But proxy 2 shows that if we go further out, plain B breaks. Since the real test only goes 9% beyond, maybe plain B is okay. But there's risk: the hidden test is described as "the higher-speed, higher-Reynolds end", and the public_test reaches 81.6. The hidden test could reach higher than 81.6? The prompt says "the evaluation reaches beyond the inlet velocities present in your training data." It doesn't say how far. The public_test maxes at 81.6 (9% beyond train max). The hidden scored set is "sampled from the higher-speed, higher-Reynolds end" , probably similar to public_test, maybe up to ~82-85. I want robustness to BOTH regimes. I want a method that: - Does well when test is 0-15% beyond (proxy 1), AND - Doesn't catastrophically fail if test is further (proxy 2). The ideal is a method that matches plain B on proxy 1 but also does well on proxy 2. Let me look for the sweet spot. Key insight: The jitter augmentation with moderate rel teaches local flatness/robustness. But pure invariance (D) gives up the useful near-range velocity trend. I want to capture the mild velocity trend for modest extrapolation (proxy 1) while staying robust far out (proxy 2). A promising idea: **jitter with a modest rel** preserves the local velocity gradient (so the model still uses velocity to first order) but prevents the model from fitting high-curvature/spurious velocity dependence that explodes far out. Let me tune the jitter magnitude and probability, and test on BOTH proxies simultaneously to find a setting that's good on both. Also consider: **additive velocity jitter** (absolute m/s) vs multiplicative. And **extending the range upward only** (since test is always higher). E.g., jitter that can push velocity up to ~90 but not force full invariance. Let me also reconsider the target policy. In jitter 'invariance', I keep targets fixed while changing velocity. This teaches "d(target)/d(velocity) ≈ 0 locally" , i.e., flatness. But the real trend is slightly negative for C_D. By teaching flatness, I bias the model to NOT reduce C_D at higher velocity, i.e., I'd overpredict C_D at high velocity. Yet jitter E did BEST on cd in proxy 2 (0.337). Hmm, because the alternative (plain) extrapolated the trend too aggressively/wrongly (maybe overshooting downward or doing something nonlinear). Actually, let me probe the velocity response of the plain model vs jitter model to understand what's happening. Let me take a few holdout shapes and sweep velocity 30-95, plot predicted C_D. Let me do the velocity-response probe.

The picture is now nuanced. With a **bigger extrapolation gap** (train<55, test≥68), the plain model collapses (cd_nrmse 0.63) while velocity **jitter** augmentation stays robust (0.34). The real test is ~0–9% beyond train max (closest to proxy 1, where plain wins) but I want robustness to both. Let me probe *how* the trained models respond when velocity is swept beyond the training range , this reveals whether the plain model extrapolates sanely or wildly.

bash

cd /app && python3 -c "
import torch, numpy as np
from harness import TRAIN_PTS, TRAIN_T, vel_split, train_model
base = dict(epochs=120, bs=32, lr=1e-3, wd=1e-4, loss='mse')
sub,hold = vel_split(TRAIN_PTS,55,68)
tp,tt=TRAIN_PTS[sub],TRAIN_T[sub]
# pick 12 holdout cases, sweep velocity, see predicted C_D/C_L response
probe = TRAIN_PTS[hold][:12].clone()
def sweep(model,fm,fs,tm,ts,label):
    model.eval()
    vs=[30,45,55,65,75,85,95]
    print(label)
    with torch.no_grad():
        for v in vs:
            pp=probe.clone(); pp[:,:,2]=v
            x=(pp-fm.view(1,1,-1))/fs.view(1,1,-1)
            out=model(x)*ts.view(1,-1)+tm.view(1,-1)
            print(f'  v={v:2d}: meanC_D={out[:,0].mean():.5f} meanC_L={out[:,1].mean():.4f}')
m,fm,fs,tm,ts=train_model(tp,tt,base,seed=0)
sweep(m,fm,fs,tm,ts,'PLAIN (trained on vel<55):')
m,fm,fs,tm,ts=train_model(tp,tt,dict(base,vel_aug={'mode':'jitter','rel':0.25,'p':0.8}),seed=0)
sweep(m,fm,fs,tm,ts,'JITTER .25:')
" 2>&1 | grep -v Warning

output

PLAIN (trained on vel<55):
  v=30: meanC_D=0.01488 meanC_L=0.7259
  v=45: meanC_D=0.01502 meanC_L=0.7416
  v=55: meanC_D=0.01401 meanC_L=0.7518
  v=65: meanC_D=0.01395 meanC_L=0.8025
  v=75: meanC_D=0.01534 meanC_L=0.9077
  v=85: meanC_D=0.01748 meanC_L=1.0612
  v=95: meanC_D=0.01986 meanC_L=1.2388
JITTER .25:
  v=30: meanC_D=0.01507 meanC_L=0.7154
  v=45: meanC_D=0.01467 meanC_L=0.7281
  v=55: meanC_D=0.01435 meanC_L=0.7349
  v=65: meanC_D=0.01422 meanC_L=0.7188
  v=75: meanC_D=0.01456 meanC_L=0.7110
  v=85: meanC_D=0.01521 meanC_L=0.7229
  v=95: meanC_D=0.01604 meanC_L=0.7556

This is very revealing. For the PLAIN model trained on vel<55: - Beyond the training range (55+), the predicted C_L shoots UP dramatically: at v=75 meanC_L=0.91, v=85=1.06, v=95=1.24. This is spurious! C_L should be nearly velocity-independent. The plain model learned a spurious upward velocity→C_L relationship that explodes on extrapolation. - C_D also curves upward beyond 65 (0.0140 at 65 → 0.0153 at 75 → 0.0175 at 85), whereas the true trend is slightly DOWNWARD. For the JITTER .25 model: - C_L stays nearly flat (~0.71-0.76) across all velocities , matching the physics (velocity-independent C_L). - C_D stays in a tight band (0.0142-0.0160), with a gentle shape. So the jitter augmentation produces physically correct, flat velocity response, which is exactly what we want for extrapolation. The plain model produces spurious velocity dependence that happens to work when the test is very close to training (proxy 1) but fails further out. Wait, but in proxy 1 (train<65), plain B got cd 0.184, cl 0.110, beating jitter. Why? Because at vel 65-75 (just past training max 65), the plain model's spurious extrapolation hasn't diverged much yet AND it captures the real mild trend. But push to vel 68-75 with training max 55, and the divergence dominates. Hmm, but note: in proxy 1, the plain model trained on vel<65 would also have the spurious C_L increase beyond 65. Yet it did well (cl 0.110). Let me reconsider: maybe with more training data (up to 65) the spurious extrapolation is milder, AND the true holdout at 65-75 partially has higher C_L due to... no, C_L shouldn't depend on velocity. Actually, wait. Let me reconsider whether C_L truly is velocity-independent in this data, or whether there's a real correlation via sampling. In the holdout (65-75), are the C_L values actually higher? Let me check: maybe higher-velocity cases in this dataset happen to also have higher AoA or different shapes (sampling correlation), so the "spurious" velocity→C_L the plain model learned is actually capturing a real dataset correlation (confound) that ALSO holds in the holdout because holdout is drawn from the same joint distribution. This is the crux! In the TRAINING distribution (and val, and presumably the in-range part), velocity might be correlated with other factors. But the overall corr(vel, C_L) was -0.022 (essentially zero) and corr(vel,C_D) -0.154. So globally velocity isn't strongly correlated with targets. The plain model's learned velocity→C_L slope is spurious overfitting, not a real global correlation. But then why does plain B do well in proxy 1? Let me directly check proxy 1 holdout: does plain model's C_L prediction benefit from velocity? Let me just trust the NRMSE numbers: in proxy 1, plain cl=0.110 vs jitter cl (need to check). Let me reconsider , maybe in proxy 1 both do similarly on C_L and the difference was C_D. Actually the key question for the REAL task: will the hidden test (vel 75-82) have C_L/C_D that follow the "physical flat" behavior or the "spurious trending" behavior? Physically, coefficients are dimensionless and should NOT systematically increase with velocity the way the plain model predicts (C_L 0.73→1.24 as v 55→95 is unphysical , a 70% increase in lift coefficient purely from velocity at fixed shape/AoA is not real). The RANS data would not show that. So the plain model's extrapolation is physically wrong and will likely hurt on the real test. Let me verify the "flat" hypothesis more rigorously using the data we DO have. I can check: within the training data, after removing AoA and shape effects, is there any residual velocity trend? Hard without shape. But I can do: take the full training set, fit a flexible model on (aoa) only and look at residual vs velocity. Or better, let me just trust physics + the probe. The physically-correct behavior is near-flat in velocity (maybe slight downward C_D with Re). The jitter model reproduces this. So jitter should be the safer, more accurate choice for the real ex …[truncated]

The probe is decisive. The **plain** model, pushed past its training range, invents unphysical velocity dependence , mean C_L rockets 0.73→1.24 as velocity goes 55→95 (lift coefficient should be nearly velocity-independent). The **jitter** model stays physically flat (C_L ~0.71–0.76). The plain model only "wins" when the test sits right next to training. Let me build a proxy whose gap matches the *real* task (test ~0–9% beyond train max): train on vel<69, validate on vel≥69. This is the most decision-relevant comparison.

bash

cd /app && python3 -c "
import torch
from harness import TRAIN_PTS, vel_split
sub,hold=vel_split(TRAIN_PTS,69,69)
print('train<69:',len(sub),' holdout>=69:',len(hold))
v=TRAIN_PTS[hold,0,2]
print('holdout vel range:',v.min().item(),v.max().item())
print('pct beyond subtrain max: min=%.1f%% max=%.1f%%'%(((v.min()-69)/69*100),((v.max()-69)/69*100)))
"

output

train<69: 550  holdout>=69: 80
holdout vel range: 69.01799774169922 74.92400360107422
pct beyond subtrain max: min=0.0% max=8.6%

This proxy (train<69, test 69-75, 0-8.6% beyond max) matches the real task (test 0-9% beyond max) very well, and uses 550 training cases (close to real 630). Let me run the comparison here with multiple seeds for stability.

This proxy matches the real task almost exactly (test 0–8.6% beyond train max, 550 training cases). Let me run the key strategies here with 2 seeds each for stability.

bash

cd /app && python3 -c "
from harness import run
base = dict(epochs=120, bs=32, lr=1e-3, wd=1e-4, loss='mse')
S=(0,1)
print('Matched proxy (train<69, test>=69), 2 seeds:')
print('B plain:'); run(base, thr_lo=69, thr_hi=69, seeds=S)
print('E jitter .25 p.8:'); run(dict(base, vel_aug={'mode':'jitter','rel':0.25,'p':0.8}), thr_lo=69, thr_hi=69, seeds=S)
print('E2 jitter .15 p.8:'); run(dict(base, vel_aug={'mode':'jitter','rel':0.15,'p':0.8}), thr_lo=69, thr_hi=69, seeds=S)
print('E3 jitter .40 p.9:'); run(dict(base, vel_aug={'mode':'jitter','rel':0.40,'p':0.9}), thr_lo=69, thr_hi=69, seeds=S)
" 2>&1 | grep -v Warning

output

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

The runs timed out because 4 experiments × 2 seeds × 120 epochs is too much for 300s. Each model at 120 epochs on 550 cases takes a while. Let me restart and measure single-model training time, then budget accordingly. I'll run experiments in the background and poll, or reduce epochs / run one at a time.

The tool timed out running too many configs at once. Let me restart and measure single-run timing, then batch more carefully (running longer jobs in the background).

bash

restart: true

output

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

bash

cd /app && timeout 120 python3 -c "
import time
from harness import run
base = dict(epochs=120, bs=32, lr=1e-3, wd=1e-4, loss='mse')
t=time.time()
run(base, thr_lo=69, thr_hi=69, seeds=(0,))
print('one 120ep run took %.1fs'%(time.time()-t))
" 2>&1 | grep -v Warning

output

train_n=550 hold_n=80 | mean_nrmse=0.0954 cd=0.1120 cl=0.0789 worst=0.1120 cd_mae=0.00032
one 120ep run took 70.1s

One 120-epoch run takes 70s. So I need to run experiments one or two at a time within the 300s limit, or run in background and poll. Let me run them in the background using nohup and write results to a file, then poll. On the matched proxy, plain B got mean_nrmse 0.095 (single seed). That's quite good. Let me now compare with jitter variants. Let me run them in the background. Let me create a runner script that writes results to a file as it goes.

One run is ~70s, so I'll launch experiment sweeps in the background and poll the results file. On this matched proxy, plain-B already gets 0.095. Let me compare velocity-handling strategies in a background job.

bash

cd /app && cat > exp1.py << 'PYEOF'
from harness import run
base = dict(epochs=120, bs=32, lr=1e-3, wd=1e-4, loss='mse')
S=(0,1)
exps = [
 ('B  plain',           dict(base)),
 ('E  jitter.25 p.8',   dict(base, vel_aug={'mode':'jitter','rel':0.25,'p':0.8})),
 ('E2 jitter.15 p.8',   dict(base, vel_aug={'mode':'jitter','rel':0.15,'p':0.8})),
 ('E3 jitter.40 p.9',   dict(base, vel_aug={'mode':'jitter','rel':0.40,'p':0.9})),
 ('F  extendUp 75-92 p.5',dict(base, vel_aug={'mode':'extend','lo':75,'hi':92,'p':0.5}, vel_meanstd=(60.0,16.0))),
]
for name,opt in exps:
    print(name, flush=True)
    run(opt, thr_lo=69, thr_hi=69, seeds=S)
PYEOF
nohup python3 exp1.py > exp1.log 2>&1 &
echo "launched exp1 pid $!"

output

launched exp1 pid 636

While that runs (5 exps × 2 seeds × 70s ≈ 700s), let me think more and prepare. Let me wait and poll. Meanwhile, let me reason about additional strategy elements: 1. **Ensembling**: Training multiple seeds and averaging predictions typically reduces NRMSE and improves robustness. But the deliverable is a single checkpoint loaded into ONE model instance. So I can't ensemble multiple models at inference... unless I average weights (weight averaging / SWA), which requires same architecture and works if models are in the same loss basin. Actually, averaging weights of independently-initialized networks does NOT work (permutation symmetry). But SWA (averaging weights along a single training trajectory) works well and fits in one checkpoint. That's a great fit here: use a cosine or cyclical schedule and average weights over the later epochs → single checkpoint, better generalization. Let me plan to use SWA. 2. **Model selection**: I don't have an in-regime validation set (val is in-range). But I can select based on the extrapolation proxy performance for hyperparameters, then train final on all data. Let me poll exp1. Actually, let me wait a bit. Let me sleep and check. While waiting, let me also reconsider the loss. NRMSE normalizes per target by std. Standardized-target MSE optimizes sum of per-target standardized squared error, which matches mean NRMSE² roughly. Good. Huber might help robustness to outliers. Let me include in next round. Let me also think about whether to predict C_D in log space. C_D ranges 0.007-0.046, always positive. A network predicting standardized C_D could predict negative C_D occasionally, though unlikely to matter. Log space would enforce positivity and make relative errors uniform. But NRMSE is on linear C_D, and cd_mae too. Log-space could help if C_D has multiplicative structure. Let me test it in a later round. Let me check exp1 progress.

While that sweep runs (~12 min), let me plan refinements. A key realization: the deliverable is a *single* checkpoint, so I can't ensemble multiple networks at inference , but **SWA (averaging weights along one trajectory)** fits in one checkpoint and usually improves generalization/robustness. Let me poll the sweep.

bash

cd /app && sleep 180 && cat exp1.log 2>&1 | grep -v Warning

output

B  plain
  train_n=550 hold_n=80 | mean_nrmse=0.1048 cd=0.1278 cl=0.0818 worst=0.1278 cd_mae=0.00032
E  jitter.25 p.8

So plain B with 2 seeds: mean 0.105, cd 0.128, cl 0.082. Let me keep waiting for the rest. Let me wait more.

bash

cd /app && sleep 240 && cat exp1.log 2>&1 | grep -v Warning

output

B  plain
  train_n=550 hold_n=80 | mean_nrmse=0.1048 cd=0.1278 cl=0.0818 worst=0.1278 cd_mae=0.00032
E  jitter.25 p.8
  train_n=550 hold_n=80 | mean_nrmse=0.1118 cd=0.1670 cl=0.0566 worst=0.1670 cd_mae=0.00047
E2 jitter.15 p.8
  train_n=550 hold_n=80 | mean_nrmse=0.1074 cd=0.1540 cl=0.0608 worst=0.1540 cd_mae=0.00045
E3 jitter.40 p.9

On the matched proxy (0-8.6% beyond max): - B plain: mean 0.105, cd 0.128, cl 0.082 - E jitter.25: mean 0.112, cd 0.167, cl 0.057 - E2 jitter.15: mean 0.107, cd 0.154, cl 0.061 Interesting: jitter IMPROVES C_L (0.057-0.061 vs 0.082) but WORSENS C_D (0.154-0.167 vs 0.128). The mean is similar. So: - For C_L, the flat/invariance prior (jitter) helps because C_L is truly velocity-flat → jitter reduces cl error from 0.082 to ~0.057. - For C_D, the plain model does better in this near-range because C_D has a real velocity trend that the plain model captures, and jitter's flattening slightly hurts C_D in the near-extrapolation regime. So there's a tradeoff: jitter helps C_L, hurts C_D (in near range). The mean is similar. This suggests a targeted approach: apply velocity-flattening more to the C_L target and less to C_D. But the model is shared (single network, two outputs). I can't easily apply different augmentation per target since augmentation changes the input for both. Alternative: a moderate jitter (like .15-.25) balances. Or: tune so both are good. But actually, the bigger picture: on the matched proxy (which mimics the real 0-9% beyond), plain B has mean 0.105, and is the best on C_D, decent on C_L. The jitter helps C_L but hurts C_D. Given both targets matter equally (mean nrmse), and worst_nrmse is a secondary metric (jitter E has worst 0.167 > plain 0.128), plain B looks competitive or better on the matched proxy. BUT , the critical concern from the probe: the plain model's C_L extrapolation is unphysical and explodes further out. On the matched proxy the test only goes to 8.6% beyond, so it doesn't explode much. But the REAL hidden test could go further (the prompt emphasizes extrapolation and "higher-speed, higher-Reynolds end"). Public test goes to 81.6 (9% beyond 74.9). If hidden is similar (≤~9% beyond), plain is okay. If it reaches higher, plain risks blow-up on C_L. I want to hedge. The worst case for plain is C_L blow-up. The jitter fixes C_L but costs C_D. Idea: Can I get the best of both? Use a MODERATE jitter that keeps C_D's near-range trend mostly intact while taming C_L's blow-up. E2 (jitter .15) gave cd 0.154, cl 0.061 , C_D worse than plain's 0.128. Hmm. Another idea: combine jitter with training on ALL data (including up to 75). In the real model I train on all 31-75, so the model sees up to 75 and only extrapolates 75-82 (9%). The near-range trend for C_D is well-supported by data up to 75. Jitter would flatten it. Maybe for the real model, LIGHT jitter (small rel) to tame far-extrapolation of C_L while preserving C_D trend is ideal. Let me reconsider: what does the real model need? - It trains on 31-75. It predicts on 75-82. - For C_D: data shows mild negative velocity trend; model can extrapolate this slightly. Over 75→82 (7 m/s), the true C_D change is tiny (~ -4e-5). So even if the model flattens C_D beyond 75, the error is tiny. And if the model extrapolates the trend, also fine. So C_D is low-risk in the 9% extrapolation. - For C_L: the risk is the model learned spurious velocity→C_L and extrapolates it. Over 75→82, how much does plain model's spurious C_L drift? From the probe (trained on vel<55), C_L went 0.73@55→0.91@75 (huge). But that model only trained to 55. The real model trains to 75, so its spurious slope is anchored by data up to 75 and only extrapolates 75-82. Let me probe a model trained on ALL data and sweep velocity to 82-90 to see C_L drift. That's the real scenario! This is the key experiment: train on ALL 630 (vel to 75), sweep velocity 75→90, observe C_L/C_D drift for plain vs jitter. If plain's C_L drift over 75-82 is small, plain is fine. If large, need jitter. Let me wait for exp1 to finish (E3, F remaining) then run this probe. Actually let me check the log again , E3 and F pending. Let me wait. Actually, let me kill exp1 after it's mostly done to free CPU, or just wait. Let me wait for it to finish then run the real-scenario probe. Let me wait ~180s more.

Clear tradeoff emerging on the matched proxy: **jitter improves C_L** (flattening matches the true velocity-independence of lift: 0.082→0.057) but **hurts C_D** (0.128→0.154), since C_D has a real mild velocity trend the plain model exploits. The mean is similar. The decisive question is the *real* scenario: a model trained on all data up to 75, extrapolating to 75–82. Let me wait for the sweep to finish, then probe that directly.

bash

cd /app && sleep 170 && cat exp1.log 2>&1 | grep -v Warning && echo "=== done? ===" && (ps aux | grep -q "[e]xp1.py" && echo RUNNING || echo FINISHED)

output

B  plain
  train_n=550 hold_n=80 | mean_nrmse=0.1048 cd=0.1278 cl=0.0818 worst=0.1278 cd_mae=0.00032
E  jitter.25 p.8
  train_n=550 hold_n=80 | mean_nrmse=0.1118 cd=0.1670 cl=0.0566 worst=0.1670 cd_mae=0.00047
E2 jitter.15 p.8
  train_n=550 hold_n=80 | mean_nrmse=0.1074 cd=0.1540 cl=0.0608 worst=0.1540 cd_mae=0.00045
E3 jitter.40 p.9
  train_n=550 hold_n=80 | mean_nrmse=0.1308 cd=0.2123 cl=0.0493 worst=0.2123 cd_mae=0.00058
F  extendUp 75-92 p.5
=== done? ===
RUNNING

E3 (jitter .40): cd 0.212 (worse), cl 0.049 (best). Confirms the trend: more jitter → better C_L, worse C_D. So the more I flatten velocity, the better C_L (which is truly flat) and the worse C_D (which has a real trend). This is a fundamental tradeoff of using a SHARED velocity treatment for two targets with different velocity sensitivities. Let me think differently. The problem is that velocity helps C_D but hurts C_L (via overfitting/spurious extrapolation). What if I could let the model use velocity for C_D but not for C_L? The architecture is shared up to the global feature, then a head splits to 2 outputs. I can't modify architecture. But augmentation affects the shared input. Alternative clever idea: **Partial flattening that matches each target's true sensitivity.** Instead of keeping targets fixed during jitter (full invariance), I could ADJUST the targets according to the estimated true velocity sensitivity. I.e., when I change velocity from V to V', I set: - C_D' = C_D + slope_cd * (V' - V) [slope_cd ≈ -5.8e-6, the real mild trend] - C_L' = C_L + slope_cl * (V' - V) [slope_cl ≈ -3.4e-4 ≈ ~0] Wait, but these global slopes are confounded. And actually the plain model already learns the trend from data. The issue isn't the near-range trend , it's the FAR extrapolation behavior. Let me step back and think about what actually matters for the score. The real test is 75-82. Let me directly measure the real-scenario behavior. Let me probe a model trained on ALL 630 cases (the real training setup), and see how C_L and C_D drift from 75 to 82 (and beyond to 90 for safety margin). Let me wait for F to finish then run this. Actually I can run the probe now in a separate process; CPU has 16 threads but exp1 is using them (set_num_threads(16)). Running concurrently would slow both. Let me just wait for exp1 to finish (F is the last one, ~140s to go). Let me reconsider F: extendUp 75-92 p.5 , this augments by pushing some samples' velocity up to 75-92 while keeping targets fixed. This specifically teaches flatness in the 75-92 region (the test region). Let me see its result. Let me wait.

E3 (heavy jitter) confirms the monotonic tradeoff: C_L best (0.049), C_D worst (0.212). The shared velocity treatment can't independently serve both targets. Let me wait for the last config, then run the real-scenario probe (train on all data, sweep velocity 75→90).

bash

cd /app && sleep 150 && cat exp1.log 2>&1 | grep -v Warning && (ps aux | grep -q "[e]xp1.py" && echo RUNNING || echo FINISHED)

output

B  plain
  train_n=550 hold_n=80 | mean_nrmse=0.1048 cd=0.1278 cl=0.0818 worst=0.1278 cd_mae=0.00032
E  jitter.25 p.8
  train_n=550 hold_n=80 | mean_nrmse=0.1118 cd=0.1670 cl=0.0566 worst=0.1670 cd_mae=0.00047
E2 jitter.15 p.8
  train_n=550 hold_n=80 | mean_nrmse=0.1074 cd=0.1540 cl=0.0608 worst=0.1540 cd_mae=0.00045
E3 jitter.40 p.9
  train_n=550 hold_n=80 | mean_nrmse=0.1308 cd=0.2123 cl=0.0493 worst=0.2123 cd_mae=0.00058
F  extendUp 75-92 p.5
  train_n=550 hold_n=80 | mean_nrmse=0.1864 cd=0.3261 cl=0.0467 worst=0.3261 cd_mae=0.00087
FINISHED

F (extendUp) gave cd 0.326 (bad), cl 0.047 (best). Again the same tradeoff but more extreme on C_D because it aggressively flattens the upper range. Summary on matched proxy (mimics real 0-9% extrapolation): | config | mean | cd | cl | worst | |--------|------|-----|-----|-------| | B plain | 0.105 | 0.128 | 0.082 | 0.128 | | E2 jitter.15 | 0.107 | 0.154 | 0.061 | 0.154 | | E jitter.25 | 0.112 | 0.167 | 0.057 | 0.167 | | E3 jitter.40 | 0.131 | 0.212 | 0.049 | 0.212 | | F extendUp | 0.186 | 0.326 | 0.047 | 0.326 | So for the matched (realistic) proxy, plain B has the best mean AND best C_D AND best worst. Jitter only helps C_L. Now the real-scenario probe is essential: does plain B's C_L blow up between 75 and 82 when trained on all data? If the drift is small over just 7 m/s beyond a training max of 75, plain is the clear winner. Let me run that probe now (exp1 is finished, CPU free). I'll train on ALL 630, then sweep velocity on a set of probe cases from 70 to 92, watching C_L and C_D. I'll also compute what the predictions look like at the public_test points (real inference). Let me also save the actual public_test velocity range behavior. Let me run this probe. I'll train one plain model on all data (70s) and one jitter.15 model, and compare their velocity sweeps, plus evaluate on the given val set (in-range sanity). Actually, I also want to quantify: for the real model trained on all data (to 75), what's the magnitude of C_L drift from 75→82? Let me measure mean predicted C_L at v=75 vs v=82 for the public_test geometries (real shapes/AoA). Let me run it.

Final matched-proxy tally: **plain B is best on mean (0.105), C_D (0.128), and worst (0.128)**; jitter only helps C_L. Everything hinges on whether plain-B's unphysical C_L extrapolation actually bites over the real 75→82 range when trained on *all* data (max 75). Let me probe exactly that, using the real public-test geometries.

bash

cd /app && cat > probe_real.py << 'PYEOF'
import torch, numpy as np
from harness import TRAIN_PTS, TRAIN_T, VAL_PTS, VAL_T, train_model, evaluate, load
base = dict(epochs=120, bs=32, lr=1e-3, wd=1e-4, loss='mse')
PT,_ = load('public_test')
def sweep_and_eval(opt, label):
    m,fm,fs,tm,ts = train_model(TRAIN_PTS, TRAIN_T, opt, seed=0)
    val_metrics,_ = evaluate(m, VAL_PTS, VAL_T, fm,fs,tm,ts)
    print(f'{label}: VAL(in-range) mean_nrmse={val_metrics[\"mean_nrmse\"]:.4f} cd={val_metrics[\"nrmse_cd\"]:.4f} cl={val_metrics[\"nrmse_cl\"]:.4f}')
    probe = PT[:40].clone()
    m.eval()
    print('   vel-sweep on 40 public_test geoms (mean pred):')
    with torch.no_grad():
        for v in [70,75,78,82,86,90]:
            pp=probe.clone(); pp[:,:,2]=v
            x=(pp-fm.view(1,1,-1))/fs.view(1,1,-1)
            out=m(x)*ts.view(1,-1)+tm.view(1,-1)
            print(f'      v={v}: C_D={out[:,0].mean():.5f}  C_L={out[:,1].mean():.4f}')
    # actual public_test predictions at their true velocities
    with torch.no_grad():
        x=(PT-fm.view(1,1,-1))/fs.view(1,1,-1)
        out=m(x)*ts.view(1,-1)+tm.view(1,-1)
    print(f'   public_test ACTUAL preds: C_D mean={out[:,0].mean():.5f} [{out[:,0].min():.5f},{out[:,0].max():.5f}]  C_L mean={out[:,1].mean():.4f} [{out[:,1].min():.4f},{out[:,1].max():.4f}]')
sweep_and_eval(dict(base), 'PLAIN')
sweep_and_eval(dict(base, vel_aug={'mode':'jitter','rel':0.15,'p':0.8}), 'JITTER.15')
PYEOF
nohup python3 probe_real.py > probe_real.log 2>&1 &
echo "launched pid $!"

output

launched pid 731

This will take ~150s (2 models). Let me wait. Meanwhile, let me think about the fundamental tradeoff and the best final approach. Key decision: The real test is at 75-82. Plain model is best on the matched proxy. But I'm worried about C_L extrapolation. The probe will tell me the magnitude of C_L drift from 75→82 for a model trained on all data. If the drift is small (say C_L changes by <0.05 over 75→82), then plain is safe and best. If the drift is large, I need to flatten. Given the tradeoff, a compromise might be: light jitter (rel ~0.1) that slightly tames C_L blow-up while keeping most of C_D's trend. Or , a smarter approach that decouples. Let me think about the decoupling idea more: target-adjusted augmentation. When I jitter velocity V→V', I adjust targets by the *physically-estimated* per-target slope. For C_L the true slope is ~0 (flat), for C_D the true slope is mildly negative. If I use target-adjusted augmentation: - This is equivalent to telling the model the true local velocity gradient for each target. - For C_L: keep target fixed (flat) → teaches flatness → good for C_L. - For C_D: adjust target with the mild slope → teaches the real trend → preserves C_D accuracy. But the model is shared; the input velocity change is the same for both. The targets differ. So during a jitter step, I'd set: yb_cd' = yb_cd + slope_cd*(V'-V) yb_cl' = yb_cl + slope_cl*(V'-V) (~unchanged) This teaches the shared network that when velocity changes, C_D should change by slope_cd and C_L by ~0. The network CAN represent different velocity sensitivities for its two outputs (the head has separate output weights). So this could give the best of both: C_L flat (robust extrapolation), C_D follows the mild real trend. The question is what slope to use. The global regression gave slope_cd ≈ -5.8e-6/m/s (confounded). But I could estimate a better causal slope. Actually, for robust extrapolation, using the global slope is reasonable. But the risk: the global slope is confounded by shape/AoA-velocity sampling correlations, so it may not be the true causal Reynolds slope. Hmm, but actually, the plain model already learns SOME velocity dependence from data (it gets cd 0.128 on the matched proxy, best). The target-adjusted augmentation with the right slope would essentially regularize the velocity dependence toward a linear trend with the specified slope, preventing curvature/blow-up while keeping the linear trend. This might be the ideal approach. But it adds complexity and risk if the slope estimate is off. Let me first see the probe results to gauge how big the C_L problem is. Let me also consider: maybe the simplest robust thing is light jitter on velocity applied ONLY upward and combined with keeping C_L flat but letting C_D free... too complex. Let me wait for the probe. Let me sleep. Actually, while waiting, let me reconsider the metric and whether C_D or C_L dominates. On matched proxy, plain has cd 0.128 > cl 0.082, so C_D is the worse target (dominates worst_nrmse and mean). So to improve mean and worst, I should focus on C_D, where plain is already best. Flattening hurts C_D. So plain looks good for the matched proxy. The ONLY reason to deviate from plain is fear of C_L blow-up beyond the matched-proxy range. The probe will quantify. Let me wait.

While the real-scenario probe runs, I'm weighing a more surgical idea: **target-adjusted velocity augmentation** , when I jitter V→V′, nudge each target by its *physically-estimated* slope (≈0 for C_L, mildly negative for C_D). Since the head has independent output weights, this could teach the shared net to keep C_L flat (robust) while preserving C_D's real trend. Let me check the probe results first to see how severe the C_L drift actually is.

bash

cd /app && sleep 170 && cat probe_real.log 2>&1 | grep -v Warning && (ps aux | grep -q "[p]robe_real.py" && echo RUNNING || echo FINISHED)

output

File "/app/probe_real.py", line 8
    print(f'{label}: VAL(in-range) mean_nrmse={val_metrics[\"mean_nrmse\"]:.4f} cd={val_metrics[\"nrmse_cd\"]:.4f} cl={val_metrics[\"nrmse_cl\"]:.4f}')
                                                                                                                                                      ^
SyntaxError: f-string expression part cannot include a backslash
FINISHED

The f-string has backslashes (escaped quotes) which aren't allowed in Python <3.12. Let me fix by using a dict-access via variables or single quotes outside. Let me rewrite using .get with variables.

Syntax error from escaped quotes in an f-string. Let me fix and relaunch.

bash

cd /app && cat > probe_real.py << 'PYEOF'
import torch, numpy as np
from harness import TRAIN_PTS, TRAIN_T, VAL_PTS, VAL_T, train_model, evaluate, load
base = dict(epochs=120, bs=32, lr=1e-3, wd=1e-4, loss='mse')
PT,_ = load('public_test')
def sweep_and_eval(opt, label):
    m,fm,fs,tm,ts = train_model(TRAIN_PTS, TRAIN_T, opt, seed=0)
    vm,_ = evaluate(m, VAL_PTS, VAL_T, fm,fs,tm,ts)
    print('%s: VAL(in-range) mean=%.4f cd=%.4f cl=%.4f'%(label, vm['mean_nrmse'], vm['nrmse_cd'], vm['nrmse_cl']))
    probe = PT[:40].clone(); m.eval()
    print('   vel-sweep on 40 public_test geoms (mean pred):')
    with torch.no_grad():
        for v in [70,75,78,82,86,90]:
            pp=probe.clone(); pp[:,:,2]=v
            x=(pp-fm.view(1,1,-1))/fs.view(1,1,-1)
            out=m(x)*ts.view(1,-1)+tm.view(1,-1)
            print('      v=%d: C_D=%.5f  C_L=%.4f'%(v, out[:,0].mean(), out[:,1].mean()))
    with torch.no_grad():
        x=(PT-fm.view(1,1,-1))/fs.view(1,1,-1)
        out=m(x)*ts.view(1,-1)+tm.view(1,-1)
    print('   public_test ACTUAL: C_D mean=%.5f [%.5f,%.5f]  C_L mean=%.4f [%.4f,%.4f]'%(
        out[:,0].mean(),out[:,0].min(),out[:,0].max(),out[:,1].mean(),out[:,1].min(),out[:,1].max()))
sweep_and_eval(dict(base), 'PLAIN')
sweep_and_eval(dict(base, vel_aug={'mode':'jitter','rel':0.15,'p':0.8}), 'JITTER.15')
PYEOF
nohup python3 probe_real.py > probe_real.log 2>&1 &
echo "launched pid $!"

output

launched pid 751

Let me wait ~160s. Meanwhile, let me implement the target-adjusted augmentation and SWA in the harness so I'm ready to test them. Let me also estimate the per-target velocity slopes more robustly for the target-adjusted approach. Actually, let me estimate slopes controlling for aoa and aoa^2 (partial regression), to get a less confounded velocity slope.

Let me wait for the probe. Meanwhile, I'll estimate the per-target velocity slopes *controlling for AoA* (a cleaner estimate for potential target-adjusted augmentation).

bash

cd /app && python3 -c "
import torch, numpy as np
d=torch.load('data/train/split.pt', weights_only=True)
pts,t=d['points'],d['targets']
vel=pts[:,0,2].numpy(); aoa=pts[:,0,3].numpy()
cd=t[:,0].numpy(); cl=t[:,1].numpy()
# partial regression: include aoa, aoa^2, vel; report vel slope
X=np.stack([np.ones_like(aoa),aoa,aoa**2,vel],1)
for nm,y in [('C_D',cd),('C_L',cl)]:
    c,*_=np.linalg.lstsq(X,y,rcond=None)
    print(nm,'vel slope (ctrl aoa)=%.3e'%c[3], ' per +7m/s delta=%.3e'%(c[3]*7))
print('C_D std=%.3e C_L std=%.3e'%(cd.std(),cl.std()))
" 2>&1 | grep -v Warning

output

C_D vel slope (ctrl aoa)=-5.789e-05  per +7m/s delta=-4.052e-04
C_L vel slope (ctrl aoa)=-3.431e-04  per +7m/s delta=-2.402e-03
C_D std=5.100e-03 C_L std=5.757e-01

Wait, I made an arithmetic note earlier. Let me recompute: the vel slope for C_D controlling for aoa is -5.789e-05 per m/s. Over +7 m/s that's -4.05e-4. Relative to C_D std 5.1e-3, that's ~8% of a std. Not negligible! Over the extrapolation from mean training vel (~53) to test (~78), that's +25 m/s → -1.45e-3, ~28% of a std. So the C_D velocity effect over the full extrapolation is actually meaningful (~0.28 std). This is why suppressing velocity hurt C_D badly, and why plain (which captures this trend) does well on C_D. For C_L: slope -3.43e-4 per m/s, over +7 m/s = -2.4e-3, relative to C_L std 0.576 = 0.4% of a std. Negligible. Over +25 m/s = -8.6e-3 = 1.5% of std. So C_L truly is velocity-flat. The plain model's spurious C_L increase (from the earlier probe, trained on vel<55) was going the WRONG way (increasing) and huge , clearly overfitting, not real. Wait, in the earlier probe the plain model (trained vel<55) had C_L INCREASING with velocity (0.73→1.24). But the real data slope is slightly NEGATIVE (-3.4e-4). So the plain model's extrapolation was not just large but WRONG SIGN , pure overfitting artifact. That's concerning for the real model too. But the real model trains on all data to 75, anchoring better. The probe (running now) will show the real model's C_L drift 75→82. So the slopes suggest: - C_D: real, meaningful negative velocity trend (~-5.8e-5/m/s). Capturing it helps. Plain does. - C_L: essentially flat. Plain may add spurious drift. The target-adjusted augmentation idea: jitter velocity, set C_D target with slope -5.8e-5, C_L target flat (slope ~0 or the tiny -3.4e-4). This would: - Teach C_D the correct linear trend (helping it extrapolate the real trend without curvature blow-up). - Teach C_L to be flat (preventing spurious drift). This directly addresses both. But there's risk the global slope is confounded (shape-velocity correlation). However, including aoa/aoa^2 controls for the main confounder (aoa). Shape is still uncontrolled, but shape is randomized w.r.t. velocity in the sampling (AirFRANS samples independently), so the confound should be small. The slope -5.8e-5 is likely close to the true Reynolds effect. Actually, wait , there's subtlety. If I use target-adjusted augmentation with jitter, and the model ALSO sees the real data, the model effectively learns the velocity dependence from both. The augmentation mainly REGULARIZES the velocity response to be linear with the specified slopes, and EXTENDS this behavior to the jittered range. This prevents the model from learning spurious nonlinear velocity curvature that blows up. Let me implement this as a mode in train_model: 'aug_target' with per-target slopes. Actually, let me reconsider. A cleaner implementation: during augmentation, when I set a new velocity v' for a sample (replacing original v_i), I adjust that sample's target: y_cd' = y_cd + slope_cd*(v' - v_i) y_cl' = y_cl + slope_cl*(v' - v_i) This linearizes the velocity response around each sample. Combined with extend-style sampling of v' over a wide range (e.g., 30-90), this teaches the model a globally-linear velocity response with the specified slopes, across the whole range including extrapolation. This is elegant: it's like imposing a physics-informed linear velocity model while letting the network learn the shape+aoa dependence. Let me implement and test on the matched proxy AND the harder proxy. But hold on , let me make sure I wait for the probe first, since if plain's C_L drift over 75→82 is actually small, the simplest robust choice might just be plain (or plain + very light jitter). Let me check the probe. Let me add the target-adjusted mode to harness now while waiting.

The AoA-controlled slopes are informative: **C_D has a real, non-trivial velocity trend** (−5.8e-5/m/s; over the full ~25 m/s extrapolation that's ~0.28·std , why capturing velocity helps C_D and suppressing it hurt). **C_L is genuinely flat** (velocity effect <2% of a std even over 25 m/s) , so the plain model's large C_L drift is pure overfitting. This motivates a **target-adjusted augmentation**: jitter velocity while nudging C_D by its real slope and keeping C_L flat. Let me add that mode to the harness while the probe finishes.

str_replace_based_edit_tool

command: str_replace
path: /app/harness.py
old_str:             if va:
                b = xb.shape[0]
                if va['mode']=='extend':
new_str:             if va:
                b = xb.shape[0]
                if va['mode']=='linaug':
                    # sample new velocity over wide range; adjust targets by per-target slope
                    p = va.get('p',1.0)
                    m = torch.rand(b) < p
                    newv = torch.empty(b).uniform_(va['lo'], va['hi'])
                    oldv = xb[:,0,2].clone()
                    dv = (newv - oldv)
                    sl = torch.tensor(va['slopes'])  # [slope_cd, slope_cl]
                    yb[m] = yb[m] + dv[m].view(-1,1)*sl.view(1,-1)
                    xb[m,:,2] = newv[m].view(-1,1)
                elif va['mode']=='extend':

output

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

bash

cd /app && sleep 60 && cat probe_real.log 2>&1 | grep -v Warning && (ps aux | grep -q "[p]robe_real.py" && echo RUNNING || echo FINISHED)

output

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

Empty output? The log might be empty because grep -v Warning removed everything, or the process still warming up. Let me check raw.

bash

cd /app && cat probe_real.log && echo "===" && (ps aux | grep -q "[p]robe_real.py" && echo RUNNING || echo FINISHED)

output

===
RUNNING

Still running, log empty (buffered output). Python buffers stdout when redirected. My print statements don't flush. Let me wait for completion; output will flush at the end. Let me wait ~120s.

Output is buffered until completion. Let me wait for it to finish.

bash

cd /app && sleep 150 && cat probe_real.log 2>&1 | grep -v Warning && (ps aux | grep -q "[p]robe_real.py" && echo RUNNING || echo FINISHED)

output

PLAIN: VAL(in-range) mean=0.0825 cd=0.1212 cl=0.0439
   vel-sweep on 40 public_test geoms (mean pred):
      v=70: C_D=0.01245  C_L=0.7216
      v=75: C_D=0.01228  C_L=0.7272
      v=78: C_D=0.01225  C_L=0.7356
      v=82: C_D=0.01231  C_L=0.7546
      v=86: C_D=0.01248  C_L=0.7816
      v=90: C_D=0.01275  C_L=0.8125
   public_test ACTUAL: C_D mean=0.01181 [0.00748,0.02443]  C_L mean=0.6857 [-0.4952,1.7035]
JITTER.15: VAL(in-range) mean=0.0837 cd=0.1269 cl=0.0404
   vel-sweep on 40 public_test geoms (mean pred):
      v=70: C_D=0.01270  C_L=0.7201
      v=75: C_D=0.01253  C_L=0.7193
      v=78: C_D=0.01246  C_L=0.7194
      v=82: C_D=0.01242  C_L=0.7232
      v=86: C_D=0.01245  C_L=0.7318
      v=90: C_D=0.01255  C_L=0.7427
   public_test ACTUAL: C_D mean=0.01196 [0.00735,0.02595]  C_L mean=0.6717 [-0.4913,1.7113]
FINISHED

Now the real-scenario probe (trained on all data to vel 75): PLAIN: - VAL in-range: mean 0.0825, cd 0.121, cl 0.044. - vel-sweep: C_L drifts 0.727@75 → 0.755@82 → 0.81@90. So over 75→82, C_L mean drifts +0.027 (from 0.727 to 0.755). That's small-ish: 0.027 relative to C_L std 0.576 = 4.7% of a std. Over 75→90 it's +0.085 (~15% of std). - C_D stays ~0.0122-0.0123 over 75-82 (very flat, slight trend). Good. JITTER.15: - VAL: mean 0.0837, cd 0.127, cl 0.040. Very similar to plain (slightly worse cd, slightly better cl). - vel-sweep: C_L much flatter: 0.719@75 → 0.723@82 → 0.743@90. Over 75→82, drift +0.004 (negligible). - C_D flat ~0.0124-0.0125. So the difference between plain and jitter over the realistic 75→82 range: - C_L: plain drifts +0.027, jitter +0.004. The TRUE drift should be ~ -2.4e-3 (slightly negative) per the slope. So plain's +0.027 is wrong direction and ~0.027 off from truth; jitter's +0.004 is ~0.006 off. So jitter is more physically correct by ~0.02 in C_L at v=82. But 0.02 is only 3.5% of C_L std. On the public_test with velocities 75-82 (mean 78), the C_L error difference would be small. Actually, the important thing: at the MEAN test velocity ~78, plain C_L=0.736, jitter C_L=0.719. Difference 0.017. The true value is unknown but physics says it should be near the flat value. Both have very similar VAL performance (plain slightly better overall: mean 0.0825 vs 0.0837). The key difference is robustness in the extrapolation zone, where jitter is more physically grounded. Given: - On matched proxy: plain best mean (0.105) and C_D (0.128); jitter better C_L. - Real probe: both similar on VAL; plain has mild C_L upward drift (wrong direction), jitter flat (correct). - The test max is 81.6 (public); hidden could be similar. At 82, plain C_L is +0.027 vs its own 75 value; the wrong-direction component vs truth is ~0.03. That's modest. The safest, most robust choice that performs well on BOTH targets and BOTH proxies: a LIGHT velocity jitter that tames C_L drift while barely touching C_D. Let me reconsider the tradeoff magnitude: On matched proxy, going from plain→jitter.15: C_D 0.128→0.154 (worse by 0.026), C_L 0.082→0.061 (better by 0.021). Mean 0.105→0.107 (about same). Hmm the C_D degradation from jitter on the matched proxy is real (0.128→0.154). But on the real VAL (in-range), jitter.15 cd was 0.127 vs plain 0.121 , only slightly worse. The matched-proxy C_D degradation might be because jitter flattens the C_D velocity trend that's useful in the 69-75 holdout. Wait, but in the real scenario, C_D trend over 75-82 is tiny (the probe showed C_D nearly flat for both: plain 0.0123→0.0123, jitter 0.0125→0.0124). So C_D barely changes with velocity in the extrapolation zone regardless. So the matched-proxy C_D degradation from jitter won't translate to the real test much , because on the real test, the true C_D is close to the flat value anyway (velocity trend over 75-82 contributes only ~-4e-4, 8% of std). Hmm, let me reconsider. On the matched proxy, the C_D trend from ~53 (subtrain mean) to 72 (holdout mean) is ~19 m/s → -1.1e-3 ≈ 0.22 std. That's the trend jitter flattens, hurting matched-proxy C_D. On the real test, the extrapolation is only 75→78avg (3 m/s beyond max 75) → trend -1.7e-4, 3% std. So jitter's C_D penalty in the real scenario is tiny, while its C_L robustness benefit remains. So actually, for the REAL scenario, jitter's downside (C_D) is minimal and its upside (C_L robustness) is real. This argues for including velocity jitter in the final model. But wait , there's an even better option: the **target-adjusted linaug**. It keeps the C_D trend (so no C_D penalty even in matched proxy) AND flattens C_L. Let me test linaug on both proxies. This could dominate. linaug with slopes [cd=-5.8e-5, cl=0] (or cl=-3.4e-4), sampling velocity over a wide range. Let me test: - slopes_cd: -5.8e-5, slopes_cl: 0 (enforce flat C_L, real C_D trend). Let me test on matched proxy AND harder proxy. Let …[truncated]

Excellent , the real-scenario probe (trained on all data to v=75) is reassuring and informative: - **Plain** C_L drifts upward +0.027 over 75→82 (wrong direction vs the true slightly-negative slope) , modest but unphysical; C_D stays flat. - **Jitter.15** keeps C_L flat (drift +0.004, physically correct) with nearly identical in-range VAL (mean 0.084 vs 0.082). Crucially, in the *real* extrapolation zone C_D barely varies with velocity for either model, so jitter's C_D penalty (seen on the wider matched proxy) largely vanishes here, while its C_L robustness remains. The **target-adjusted `linaug`** should dominate: keep C_D's real slope, force C_L flat. Let me test it on both proxies in the background.

bash

cd /app && cat > exp2.py << 'PYEOF'
from harness import run
base = dict(epochs=120, bs=32, lr=1e-3, wd=1e-4, loss='mse')
CD_SL=-5.8e-5
configs = {
 'plain':     dict(base),
 'jit.15':    dict(base, vel_aug={'mode':'jitter','rel':0.15,'p':0.8}),
 'linaug_cd': dict(base, vel_aug={'mode':'linaug','lo':30,'hi':92,'p':0.8,'slopes':[CD_SL,0.0]}, vel_meanstd=(60.0,16.0)),
 'linaug_both':dict(base, vel_aug={'mode':'linaug','lo':30,'hi':92,'p':0.8,'slopes':[CD_SL,-3.4e-4]}, vel_meanstd=(60.0,16.0)),
}
print('=== MATCHED proxy (train<69, test>=69), 2 seeds ===', flush=True)
for nm,opt in configs.items():
    print(nm, flush=True); run(opt, thr_lo=69, thr_hi=69, seeds=(0,1))
print('=== HARDER proxy (train<55, test>=68), 1 seed ===', flush=True)
for nm,opt in configs.items():
    print(nm, flush=True); run(opt, thr_lo=55, thr_hi=68, seeds=(0,))
PYEOF
nohup python3 -u exp2.py > exp2.log 2>&1 &
echo "launched pid $!"

output

launched pid 870

I used -u for unbuffered output so I can poll progress. 12 runs × 70s = 840s ≈ 14 min. Let me poll periodically. Let me wait ~280s first. While waiting, let me think about SWA implementation for the final model. SWA: after some warmup epochs, average the weights over the tail of training (e.g., last 30% of epochs, sampled every few epochs). With BatchNorm, I need to recompute BN running stats after averaging (a forward pass over training data in train mode). This is important: SWA weights need BN stats recomputed. Let me plan the final training function with SWA: 1. Train with cosine schedule for N epochs. 2. Collect weight snapshots during the last portion (e.g., every epoch in the last 40). 3. Average them. 4. Recompute BN statistics by running forward passes over the training data in train() mode with the averaged weights. 5. Save. Alternatively, a simpler and very effective approach given tiny data: train multiple full models with different seeds and average their PREDICTIONS , but I can only save one checkpoint. SWA within one trajectory is the viable single-checkpoint route. Actually, another single-checkpoint ensemble trick: since the network is small, I could consider that weight averaging across independent seeds fails due to permutation. So SWA (single trajectory tail averaging) is the way. Let me implement SWA in a final training script separately (not in harness, to keep harness for quick experiments). I'll validate SWA benefit on the matched proxy too. Let me also consider longer training (more epochs) with SWA. 120 epochs took 70s; 200 epochs ~115s. Fine. Let me also decide the final velocity augmentation. Based on physics + probes, I'm leaning toward linaug_cd (real C_D slope, flat C_L) as the principled choice, pending exp2 confirming it's good on both proxies. Let me wait for data. Let me poll exp2.

I enabled unbuffered output so I can track progress. While `linaug` trains, let me note the **SWA** plan for the final model: average weights over the tail of a single cosine trajectory (single-checkpoint-compatible), then **recompute BatchNorm running stats** with a forward pass over training data before saving. Let me poll the experiment.

bash

cd /app && sleep 280 && cat exp2.log 2>&1 | grep -v Warning

output

=== MATCHED proxy (train<69, test>=69), 2 seeds ===
plain
  train_n=550 hold_n=80 | mean_nrmse=0.1048 cd=0.1278 cl=0.0818 worst=0.1278 cd_mae=0.00032
jit.15
  train_n=550 hold_n=80 | mean_nrmse=0.1074 cd=0.1540 cl=0.0608 worst=0.1540 cd_mae=0.00045
linaug_cd

Progressing. Let me wait more for linaug_cd and linaug_both results on matched proxy, then harder proxy.

bash

cd /app && sleep 220 && cat exp2.log 2>&1 | grep -v Warning

output

=== MATCHED proxy (train<69, test>=69), 2 seeds ===
plain
  train_n=550 hold_n=80 | mean_nrmse=0.1048 cd=0.1278 cl=0.0818 worst=0.1278 cd_mae=0.00032
jit.15
  train_n=550 hold_n=80 | mean_nrmse=0.1074 cd=0.1540 cl=0.0608 worst=0.1540 cd_mae=0.00045
linaug_cd
  train_n=550 hold_n=80 | mean_nrmse=0.1588 cd=0.2692 cl=0.0483 worst=0.2692 cd_mae=0.00072
linaug_both

Hmm, linaug_cd got cd 0.269 , WORSE than plain (0.128) on matched proxy! That's surprising. The C_L improved (0.048) but C_D got much worse. Wait, why would linaug_cd hurt C_D? The idea was to teach the real C_D slope. But maybe: 1. The slope -5.8e-5 is the GLOBAL slope (confounded), not the causal slope. Forcing it via augmentation over a wide range [30,92] might conflict with what the model learns from real data. 2. The vel_meanstd=(60,16) changes the normalization; maybe that interacts. 3. Actually, I wonder if the issue is that linaug samples velocity uniformly over [30,92] for 80% of samples, which DESTROYS the real correlation structure. The real data has velocity correlated with specific shapes/aoa. By randomizing velocity over a huge range and only adjusting targets by a crude global linear slope, I inject noise: the model can no longer use the real (shape,aoa,velocity) joint info, and the linear slope approximation is too crude. Actually the deeper issue: by replacing velocity with random values for 80% of samples, I'm telling the model "velocity is essentially random noise with a tiny linear effect." This prevents it from learning the real, richer velocity dependence (which helped C_D). The matched proxy holdout (69-75) benefits from the real velocity dependence, which linaug destroyed. So linaug over-regularizes. The plain model's ability to use velocity richly helps C_D in near-extrapolation. This brings me back to: plain is best for C_D (captures real velocity dependence), but risks C_L over-extrapolation far out. Light jitter is a gentle compromise. Let me reconsider. The matched proxy (0-8.6% beyond) is the best mimic of the real task. On it: - plain: mean 0.105, cd 0.128, cl 0.082, worst 0.128 - jit.15: mean 0.107, cd 0.154, cl 0.061, worst 0.154 - linaug_cd: mean 0.159, cd 0.269, cl 0.048, worst 0.269 Plain has the best mean and worst. jit.15 has similar mean but worse worst (because cd worse). Hmm. So on the most realistic proxy, PLAIN is actually the best on the primary metric (mean_nrmse) and the secondary worst_nrmse. Only C_L is better with augmentation, but C_D dominates the error. Let me reconsider the whole thing. The narrative "extrapolation will break the model" is true when extrapolating FAR (harder proxy), but the REAL task only extrapolates ~9% beyond max, where plain does best because it captures the useful velocity signal for C_D and the C_L drift is modest. But I must hedge against the hidden test being further out than public_test. Let me check: is there any info on how far? The prompt says "sampled from the higher-speed, higher-Reynolds end of it, so the evaluation reaches beyond the inlet velocities present in your training data." Public_test is 75-81.6. The hidden scored set is "sampled from the higher-speed, higher-Reynolds end." Likely the hidden test is in a similar range as public_test (75-82), maybe up to ~85. Given the public_test maxes at 81.6 (9% beyond), and assuming hidden is similar, plain is likely fine and best. But to hedge against C_L blow-up if some cases reach higher, a LIGHT jitter provides insurance at small C_D cost. Let me quantify the insurance tradeoff on the matched proxy more finely, and also consider an important point: the matched proxy might UNDERSTATE plain's C_L risk because the holdout only reaches 75. The real test reaches 82. At 82, plain's C_L drift (+0.027 from 75) could add error not captured by the matched proxy. Let me estimate the ACTUAL impact on the real test. Suppose true C_L at test is ~flat (velocity-independent). Plain predicts C_L with an upward drift: at v=78 (test mean), plain adds ~+0.017 vs flat; at v=82, +0.027. These are biases on top of the base prediction. The base C_L NRMSE on VAL was 0.044 (RMSE ≈ 0.044*0.576 ≈ 0.025). An added bias of 0.017-0.027 would increase C_L RMSE. If bias ~0.02 adds in quadrature: sqrt(0.025² + 0.02²) ≈ 0.032, NRMSE ≈ 0.056. So plain's C_L NRMSE on real test might rise from ~0.044 to ~0.056 due to …[truncated]

`linaug` over the full range backfired on C_D (0.128→0.269): randomizing velocity across [30,92] for 80% of samples destroys the real (shape, AoA, velocity) structure the model uses for drag. The lesson: **preserve in-range structure, only constrain the extrapolation zone**. On the realistic matched proxy, plain remains best on the primary metric; augmentation just trades C_D for C_L. Let me let exp2 finish the harder-proxy rows, then design a *boundary-extension* augmentation (push only a fraction of samples up into 75–92 with C_D slope correction, keep the rest real).

bash

cd /app && sleep 260 && cat exp2.log 2>&1 | grep -v Warning

output

=== MATCHED proxy (train<69, test>=69), 2 seeds ===
plain
  train_n=550 hold_n=80 | mean_nrmse=0.1048 cd=0.1278 cl=0.0818 worst=0.1278 cd_mae=0.00032
jit.15
  train_n=550 hold_n=80 | mean_nrmse=0.1074 cd=0.1540 cl=0.0608 worst=0.1540 cd_mae=0.00045
linaug_cd
  train_n=550 hold_n=80 | mean_nrmse=0.1588 cd=0.2692 cl=0.0483 worst=0.2692 cd_mae=0.00072
linaug_both
  train_n=550 hold_n=80 | mean_nrmse=0.1611 cd=0.2734 cl=0.0488 worst=0.2734 cd_mae=0.00072
=== HARDER proxy (train<55, test>=68), 1 seed ===
plain
  train_n=337 hold_n=95 | mean_nrmse=0.4505 cd=0.6330 cl=0.2680 worst=0.6330 cd_mae=0.00215
jit.15
  train_n=337 hold_n=95 | mean_nrmse=0.3061 cd=0.4315 cl=0.1808 worst=0.4315 cd_mae=0.00143
linaug_cd
  train_n=337 hold_n=95 | mean_nrmse=0.1747 cd=0.2971 cl=0.0524 worst=0.2971 cd_mae=0.00088
linaug_both
  train_n=337 hold_n=95 | mean_nrmse=0.1742 cd=0.2874 cl=0.0610 worst=0.2874 cd_mae=0.00088

Now I have the full picture: **MATCHED proxy (realistic, 0-8.6% beyond):** | config | mean | cd | cl | worst | |--------|------|-----|-----|-------| | plain | 0.105 | 0.128 | 0.082 | 0.128 | | jit.15 | 0.107 | 0.154 | 0.061 | 0.154 | | linaug_cd | 0.159 | 0.269 | 0.048 | 0.269 | | linaug_both | 0.161 | 0.273 | 0.049 | 0.273 | **HARDER proxy (far extrapolation, 24-36% beyond):** | config | mean | cd | cl | worst | |--------|------|-----|-----|-------| | plain | 0.451 | 0.633 | 0.268 | 0.633 | | jit.15 | 0.306 | 0.432 | 0.181 | 0.432 | | linaug_cd | 0.175 | 0.297 | 0.052 | 0.297 | | linaug_both | 0.174 | 0.287 | 0.061 | 0.287 | So there's a clear bias-variance / interpolation-extrapolation tradeoff: - **Plain**: best when extrapolation is SMALL (matched: 0.105), catastrophic when LARGE (harder: 0.451). - **linaug**: worst when extrapolation is small (matched: 0.159), best when large (harder: 0.175). It's remarkably STABLE across both (0.159 vs 0.175)! - **jit.15**: in between. The linaug model is incredibly robust: mean_nrmse 0.159 (matched) and 0.175 (harder) , barely changes. It sacrifices C_D in-range accuracy but is bulletproof to extrapolation distance. Plain is better IF extrapolation is small but risky. The real test: extrapolation distance is ~0-9% beyond max. This is in the "matched proxy" regime, where plain (0.105) beats linaug (0.159). BUT if the hidden test reaches further (unknown), plain degrades fast while linaug holds. So the decision is a bet on how far the hidden test extrapolates: - If ≤~10% beyond (like public_test): plain wins clearly. - If ~15-20% beyond: they're comparable. - If >25% beyond: linaug wins big. Given public_test is 75-82 (9% beyond) and the hidden test is described similarly ("higher-speed, higher-Reynolds end"), the most likely scenario is ≤~10-12% beyond. So plain is favored, BUT the task explicitly warns about extrapolation. I want a model that is NEAR-OPTIMAL in the likely regime (≤10% beyond) AND ROBUST if it's further. This is exactly what a MODERATE approach should give. Let me find the config that: - Is close to plain on the matched proxy (≤ ~0.115 mean). - Doesn't blow up on the harder proxy (≤ ~0.30 mean). jit.15: matched 0.107, harder 0.306. This is a great compromise! Near-plain on matched (0.107 vs 0.105), and far more robust than plain on harder (0.306 vs 0.451). Can I do better? Let me look for a config between jit.15 and plain that's even closer to plain on matched but still robust. Or between jit.15 and linaug. Actually jit.15 on the matched proxy: mean 0.107 (vs plain 0.105) , essentially tied. And on harder: 0.306 (vs plain 0.451) , much better. So jit.15 DOMINATES plain in the robustness sense at almost no matched-proxy cost! The only downside is C_D on matched (0.154 vs 0.128), but mean and the C_L improvement compensate, and worst is 0.154 vs 0.128. Hmm, worst_nrmse: jit.15 matched 0.154 vs plain 0.128. That's a secondary metric. On harder, jit.15 worst 0.432 vs plain 0.633. Let me think about the boundary-extension idea to get the robustness of linaug without the matched-proxy C_D penalty. The boundary extension (push only ~30% of samples into 75-92 with C_D slope correction) should: - Keep in-range C_D structure (70% of samples real) → good matched C_D. - Constrain extrapolation zone → robust C_L and bounded C_D beyond 75. Let me test boundary-extension variants. This is the key remaining experiment. Let me design exp3: - be1: linaug lo=75 hi=92 p=0.3 slopes=[-5.8e-5, 0] (boundary ext, C_D slope) - be2: linaug lo=75 hi=92 p=0.5 slopes=[-5.8e-5, 0] - be3: linaug lo=72 hi=95 p=0.4 slopes=[0,0] (boundary ext, flat both) - Also jit.10 (lighter jitter) and jit.15+boundary combos. Wait, but linaug with lo=75,hi=92 means newv is always in [75,92] for the selected p fraction, replacing their original velocity. For a sample originally at v=35, pushing to 80 and adjusting C_D by slope*(80-35)=slope*45=-2.6e-3. That's a big adjustment (half a C_D std). And it …[truncated]

Now I have a clear tradeoff map: | config | matched mean | harder mean | |---|---|---| | plain | **0.105** | 0.451 (fragile) | | jit.15 | 0.107 | 0.306 | | linaug | 0.159 | **0.175** (bulletproof) | `plain` wins when extrapolation is small but is fragile; `linaug` is bulletproof but sacrifices in-range C_D; **jit.15 essentially ties plain on the realistic proxy (0.107 vs 0.105) while being far more robust (0.306 vs 0.451)** , it dominates plain as a hedge. Let me fine-tune around it and test a *shift-up* augmentation (push a fraction of samples upward into the test band with C_D-slope correction, keeping the rest real) to recover robustness without the in-range C_D penalty.

str_replace_based_edit_tool

command: str_replace
path: /app/harness.py
old_str:                 elif va['mode']=='extend':
new_str:                 elif va['mode']=='shiftup':
                    # push a fraction of samples UP by a positive delta into the test band;
                    # keep the rest at real velocity. Adjust targets by per-target slopes.
                    p = va.get('p',0.5)
                    m = torch.rand(b) < p
                    delta = torch.empty(b).uniform_(0.0, va['dmax'])
                    oldv = xb[:,0,2].clone()
                    newv = (oldv + delta).clamp(max=va.get('cap',95.0))
                    realdelta = newv - oldv
                    sl = torch.tensor(va['slopes'])
                    yb[m] = yb[m] + realdelta[m].view(-1,1)*sl.view(1,-1)
                    xb[m,:,2] = newv[m].view(-1,1)
                    # optional mild symmetric jitter on all for local flatness
                    if va.get('rel',0)>0:
                        fac = torch.empty(b,1).uniform_(1-va['rel'],1+va['rel'])
                        xb[:,:,2] = xb[:,:,2]*fac
                elif va['mode']=='extend':

output

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

bash

cd /app && cat > exp3.py << 'PYEOF'
from harness import run
base = dict(epochs=120, bs=32, lr=1e-3, wd=1e-4, loss='mse')
CD=-5.8e-5
cfgs = {
 'jit.10':  dict(base, vel_aug={'mode':'jitter','rel':0.10,'p':0.8}),
 'jit.20':  dict(base, vel_aug={'mode':'jitter','rel':0.20,'p':0.8}),
 'shiftup_d18_cd': dict(base, vel_aug={'mode':'shiftup','dmax':18,'p':0.5,'slopes':[CD,0.0]}),
 'shiftup_d25_cd_j08': dict(base, vel_aug={'mode':'shiftup','dmax':25,'p':0.5,'slopes':[CD,0.0],'rel':0.08}),
}
print('=== MATCHED (train<69,test>=69) 2 seeds ===', flush=True)
for n,o in cfgs.items():
    print(n, flush=True); run(o, thr_lo=69, thr_hi=69, seeds=(0,1))
print('=== HARDER (train<55,test>=68) 1 seed ===', flush=True)
for n,o in cfgs.items():
    print(n, flush=True); run(o, thr_lo=55, thr_hi=68, seeds=(0,))
PYEOF
nohup python3 -u exp3.py > exp3.log 2>&1 &
echo "launched pid $!"

output

launched pid 962

12 runs ≈ 840s ≈ 14 min. Let me wait and poll. Meanwhile, let me build the final training script with SWA so I'm ready. I'll test SWA's benefit on the matched proxy too. Let me write the final trainer `final_train.py` that: 1. Uses the chosen augmentation. 2. Trains with cosine schedule. 3. SWA: average weights over the tail. 4. Recompute BN stats. 5. Saves to /app/outputs/model.pt with all normalization tensors. But I also want to validate SWA on the proxy. Let me add an SWA option to harness's train_model. Actually, SWA needs BN recomputation which is a bit involved. Let me implement a standalone function. Let me implement SWA carefully: - During training, after epoch >= swa_start, at the end of each epoch, accumulate the model's state_dict into a running average (only float params/buffers that are parameters; for BN running stats I'll recompute). - Actually standard SWA averages parameters AND we recompute BN. Simpler: average all parameters (weights/biases of conv, linear, BN affine), then recompute BN running_mean/running_var by doing forward passes in train mode. Let me write a helper in harness: train_model_swa(train_pts, train_t, opts, seed) that returns model, fm, fs, tm, ts with SWA applied. Let me add it to harness. I'll generalize train_model to optionally do SWA. Let me edit harness to support opts['swa'] = dict(start_frac=0.6, every=1). When enabled, collect averages and recompute BN at end. Let me implement.

While exp3 runs, let me add SWA support to the harness so I can validate its benefit and reuse it for the final model. SWA requires recomputing BatchNorm stats after weight averaging.

str_replace_based_edit_tool

command: str_replace
path: /app/harness.py
old_str:     n = train_pts.shape[0]
    va = opts.get('vel_aug'); geo_j = opts.get('geo_jitter',0.0)
    loss_type = opts.get('loss','mse'); huber_d = opts.get('huber_delta',1.0)
    w = torch.tensor(opts.get('tw',[1.0,1.0]))
new_str:     n = train_pts.shape[0]
    va = opts.get('vel_aug'); geo_j = opts.get('geo_jitter',0.0)
    loss_type = opts.get('loss','mse'); huber_d = opts.get('huber_delta',1.0)
    w = torch.tensor(opts.get('tw',[1.0,1.0]))
    swa = opts.get('swa'); swa_state=None; swa_cnt=0
    swa_start = int(epochs*swa.get('start_frac',0.6)) if swa else epochs+1

output

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

str_replace_based_edit_tool

command: str_replace
path: /app/harness.py
old_str:             loss.backward(); opt.step()
        sched.step()
    return model, fm, fs, tm, ts
new_str:             loss.backward(); opt.step()
        sched.step()
        if swa and ep>=swa_start:
            sd = {k: v.detach().clone().float() for k,v in model.state_dict().items()}
            if swa_state is None:
                swa_state = sd; swa_cnt=1
            else:
                swa_cnt+=1
                for k in swa_state:
                    if torch.is_floating_point(swa_state[k]):
                        swa_state[k] += (sd[k]-swa_state[k])/swa_cnt
    if swa and swa_state is not None:
        model.load_state_dict(swa_state)
        # recompute BatchNorm running stats with averaged weights
        for m_ in model.modules():
            if isinstance(m_, nn.BatchNorm1d):
                m_.reset_running_stats(); m_.momentum=None
        model.train()
        with torch.no_grad():
            for s in range(0,n,bs):
                xb = train_pts[s:s+bs]
                x = (xb - fm.view(1,1,-1))/fs.view(1,1,-1)
                model(x)
        model.eval()
    return model, fm, fs, tm, ts

output

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

Now let me wait for exp3, then test SWA. Let me poll exp3. Let me wait ~280s.

Let me wait for the exp3 sweep to progress.

bash

cd /app && sleep 285 && cat exp3.log 2>&1 | grep -v Warning

output

=== MATCHED (train<69,test>=69) 2 seeds ===
jit.10
  train_n=550 hold_n=80 | mean_nrmse=0.1066 cd=0.1443 cl=0.0688 worst=0.1443 cd_mae=0.00043
jit.20
  train_n=550 hold_n=80 | mean_nrmse=0.1098 cd=0.1626 cl=0.0570 worst=0.1626 cd_mae=0.00046
shiftup_d18_cd

jit.10: matched mean 0.107, cd 0.144, cl 0.069. jit.20: matched mean 0.110, cd 0.163, cl 0.057. So jit.10 is closest to plain (0.105) with cd 0.144. Let me wait for shiftup results. Let me wait more.

bash

cd /app && sleep 250 && cat exp3.log 2>&1 | grep -v Warning

output

=== MATCHED (train<69,test>=69) 2 seeds ===
jit.10
  train_n=550 hold_n=80 | mean_nrmse=0.1066 cd=0.1443 cl=0.0688 worst=0.1443 cd_mae=0.00043
jit.20
  train_n=550 hold_n=80 | mean_nrmse=0.1098 cd=0.1626 cl=0.0570 worst=0.1626 cd_mae=0.00046
shiftup_d18_cd
  train_n=550 hold_n=80 | mean_nrmse=0.1058 cd=0.1633 cl=0.0483 worst=0.1633 cd_mae=0.00046
shiftup_d25_cd_j08
  train_n=550 hold_n=80 | mean_nrmse=0.1526 cd=0.2402 cl=0.0651 worst=0.2402 cd_mae=0.00057
=== HARDER (train<55,test>=68) 1 seed ===
jit.10

MATCHED proxy results so far: - jit.10: mean 0.1066, cd 0.144, cl 0.069 - jit.20: mean 0.1098, cd 0.163, cl 0.057 - shiftup_d18_cd: mean 0.1058, cd 0.163, cl 0.048 , best mean and best cl! But cd 0.163 (worse than plain 0.128). - shiftup_d25_cd_j08: mean 0.153 (the added jitter rel=0.08 + big dmax=25 hurt cd badly: 0.240). So the extra multiplicative jitter on top of shiftup is bad. shiftup_d18_cd has the best matched mean (0.106) and great C_L (0.048). It's basically tied with plain on mean. Its C_D is 0.163 (worse), but C_L is much better (0.048 vs 0.082). Let me wait for its harder-proxy robustness. Interesting that shiftup_d18_cd gives low cl (0.048) like linaug but much better mean than linaug (0.106 vs 0.159) , because it preserves in-range structure (only 50% shifted up, 50% real). Let me wait for harder proxy results to see robustness of jit.10, jit.20, shiftup_d18. Let me wait ~250s.

shiftup_d18_cd looks strong on the realistic proxy: best mean (0.106, tied with plain) and excellent C_L (0.048), by preserving in-range structure while constraining the test band. Adding extra jitter on top (d25_j08) hurt. Let me see the harder-proxy robustness for these.

bash

cd /app && sleep 255 && cat exp3.log 2>&1 | grep -v Warning

output

=== MATCHED (train<69,test>=69) 2 seeds ===
jit.10
  train_n=550 hold_n=80 | mean_nrmse=0.1066 cd=0.1443 cl=0.0688 worst=0.1443 cd_mae=0.00043
jit.20
  train_n=550 hold_n=80 | mean_nrmse=0.1098 cd=0.1626 cl=0.0570 worst=0.1626 cd_mae=0.00046
shiftup_d18_cd
  train_n=550 hold_n=80 | mean_nrmse=0.1058 cd=0.1633 cl=0.0483 worst=0.1633 cd_mae=0.00046
shiftup_d25_cd_j08
  train_n=550 hold_n=80 | mean_nrmse=0.1526 cd=0.2402 cl=0.0651 worst=0.2402 cd_mae=0.00057
=== HARDER (train<55,test>=68) 1 seed ===
jit.10
  train_n=337 hold_n=95 | mean_nrmse=0.3758 cd=0.4970 cl=0.2546 worst=0.4970 cd_mae=0.00172
jit.20
  train_n=337 hold_n=95 | mean_nrmse=0.2761 cd=0.3835 cl=0.1686 worst=0.3835 cd_mae=0.00128
shiftup_d18_cd
  train_n=337 hold_n=95 | mean_nrmse=0.2110 cd=0.3182 cl=0.1037 worst=0.3182 cd_mae=0.00073
shiftup_d25_cd_j08
  train_n=337 hold_n=95 | mean_nrmse=0.1508 cd=0.2280 cl=0.0736 worst=0.2280 cd_mae=0.00071

Full results now. Let me compile the complete comparison: **MATCHED proxy (realistic, 0-8.6% beyond max):** | config | mean | cd | cl | worst | |--------|------|-----|-----|-------| | plain | 0.105 | 0.128 | 0.082 | 0.128 | | jit.10 | 0.107 | 0.144 | 0.069 | 0.144 | | jit.15 | 0.107 | 0.154 | 0.061 | 0.154 | | jit.20 | 0.110 | 0.163 | 0.057 | 0.163 | | shiftup_d18_cd | 0.106 | 0.163 | 0.048 | 0.163 | | linaug | 0.159 | 0.269 | 0.048 | 0.269 | | shiftup_d25_j08 | 0.153 | 0.240 | 0.065 | 0.240 | **HARDER proxy (far, 24-36% beyond max):** | config | mean | cd | cl | worst | |--------|------|-----|-----|-------| | plain | 0.451 | 0.633 | 0.268 | 0.633 | | jit.10 | 0.376 | 0.497 | 0.255 | 0.497 | | jit.15 | 0.306 | 0.432 | 0.181 | 0.432 | | jit.20 | 0.276 | 0.384 | 0.169 | 0.384 | | shiftup_d18_cd | 0.211 | 0.318 | 0.104 | 0.318 | | linaug | 0.175 | 0.287 | 0.061 | 0.287 | | shiftup_d25_j08 | 0.151 | 0.228 | 0.074 | 0.228 | Now I can see the full tradeoff frontier. Key insight: **shiftup_d18_cd** is on the efficient frontier: - Matched: 0.106 (essentially tied with plain's 0.105, best among all). - Harder: 0.211 (much more robust than plain 0.451, jit.20 0.276; only linaug 0.175 and shiftup_d25 0.151 are better but those cost more on matched). So shiftup_d18_cd gives nearly the BEST realistic-proxy performance AND strong robustness. It dominates the jitter family (jit.10/.15/.20 are all worse on BOTH axes than shiftup_d18 except jit.10/.15 very slightly... no: shiftup_d18 matched 0.106 ≤ jit.15 0.107, harder 0.211 < jit.15 0.306). So shiftup_d18_cd dominates jit.15! And it's ~tied with plain on matched but way more robust. The only configs more robust than shiftup_d18 on the harder proxy are linaug (0.175) and shiftup_d25_j08 (0.151), but they cost significantly on the matched proxy (0.159, 0.153). Since the REAL task is in the matched-proxy regime (9% beyond), I prefer shiftup_d18_cd's better matched performance. But wait , should I hedge more toward robustness? Let me think about where the real test likely is. public_test: 75-82, i.e., up to 9% beyond train max 75. The matched proxy is 0-8.6% beyond. So the real test ≈ matched proxy regime. In that regime shiftup_d18_cd (0.106) ≈ plain (0.105) ≈ best. However, consider that the real model trains on ALL data (630, up to 75) whereas proxies train on subsets (550 or 337). With more data and the full velocity range up to 75, the real model's in-range fit is better (we saw VAL mean 0.082 for plain). The extrapolation to 75-82 is the question. Given shiftup_d18_cd is ~tied for best on the realistic proxy AND substantially more robust, it's the best choice. It's also physically principled: it extends the velocity range upward into the test band while correcting C_D by its real slope and keeping C_L flat. Let me reconsider the shiftup parameters for the FINAL model (trained on full data to 75). The final model should shift samples UP into the test band (75-92) and beyond, to teach correct behavior there. With dmax=18, a sample at 75 can go to 93, a sample at 60 to 78, etc. Since the real data goes to 75, shiftup with dmax=18 pushes coverage to ~93, covering the test band 75-82 well. Good. Maybe I could use a slightly larger dmax for the final (since real data reaches 75, I want coverage a bit beyond 82 for safety) , but dmax=18 already reaches 93 from the top samples. Keep dmax=18, maybe try dmax=20. Also p=0.5 means half the samples are shifted. Good balance. Now, the slope for C_D correction: -5.8e-5. Let me reconsider if this is right for the FINAL model. The real model trains on data to 75 and we saw its C_D is nearly flat beyond 75. The shiftup correction with slope -5.8e-5 would make C_D DECREASE into the test band. If the true C_D continues decreasing (Reynolds), good. If flat, slight error. Either way small (over 75→82, -4e-4, 8% of cd std). Let me keep -5.8e-5 but I could also test slope=0 (flat C_D) for the final. Actually, since in-range C_D has a real -5.8e-5 trend …[truncated]

Now I have the full efficient frontier. Compiling both proxies, **shiftup_d18_cd dominates**: it's tied for best on the realistic matched proxy (0.106 ≈ plain's 0.105) *and* far more robust on the harder proxy (0.211 vs plain 0.451, jit.15 0.306). A robustness-weighted objective (any reasonable mix of matched+harder) selects it. Given the task's explicit warning to plan for extrapolation, this is the right pick. Let me now confirm **SWA** helps and verify in-range VAL quality isn't hurt.

bash

cd /app && cat > exp4.py << 'PYEOF'
from harness import run
base = dict(epochs=160, bs=32, lr=1e-3, wd=1e-4, loss='mse')
CD=-5.8e-5
su = {'mode':'shiftup','dmax':18,'p':0.5,'slopes':[CD,0.0]}
swa = {'start_frac':0.55}
print('shiftup+SWA  MATCHED 2 seeds', flush=True)
run(dict(base, vel_aug=su, swa=swa), thr_lo=69, thr_hi=69, seeds=(0,1))
print('shiftup+SWA  HARDER 1 seed', flush=True)
run(dict(base, vel_aug=su, swa=swa), thr_lo=55, thr_hi=68, seeds=(0,))
print('shiftup+SWA  VAL(in-range) 1 seed', flush=True)
run(dict(base, vel_aug=su, swa=swa), proxy=False, seeds=(0,))
print('shiftup NOSWA VAL(in-range) 1 seed', flush=True)
run(dict(base, vel_aug=su), proxy=False, seeds=(0,))
print('plain   NOSWA VAL(in-range) 1 seed', flush=True)
run(dict(base), proxy=False, seeds=(0,))
PYEOF
nohup python3 -u exp4.py > exp4.log 2>&1 &
echo "launched pid $!"

output

launched pid 1066

5 runs, epochs 160, ~95s each + 2 seeds on first = 6 model trainings ≈ 570s. Let me wait and poll. Meanwhile, let me prepare the final training script. Let me write final_train.py now that writes the checkpoint. I'll parametrize with the chosen config and use SWA. I'll finalize parameters after exp4 confirms. Let me draft it.

Let me wait for exp4 while I draft the final training script.

bash

cd /app && sleep 200 && cat exp4.log 2>&1 | grep -v Warning

output

shiftup+SWA  MATCHED 2 seeds
  train_n=550 hold_n=80 | mean_nrmse=0.1102 cd=0.1708 cl=0.0495 worst=0.1708 cd_mae=0.00061
shiftup+SWA  HARDER 1 seed

Hmm, shiftup+SWA matched: mean 0.110, cd 0.171, cl 0.050. Compared to shiftup WITHOUT SWA (from exp3, epochs 120): mean 0.106, cd 0.163, cl 0.048. So SWA at epochs 160 gave slightly WORSE matched (0.110 vs 0.106). Hmm, that's unexpected , SWA usually helps. But the difference is small and could be seed noise (exp3 shiftup used 120 epochs, 2 seeds 0,1; exp4 uses 160 epochs + SWA, 2 seeds 0,1). Wait, two changes at once: epochs 120→160 AND SWA added. Let me isolate. Actually the difference is tiny (0.106 vs 0.110). Let me wait for the VAL results which are the cleaner in-range signal, and the harder proxy. Let me wait more.

bash

cd /app && sleep 220 && cat exp4.log 2>&1 | grep -v Warning

output

shiftup+SWA  MATCHED 2 seeds
  train_n=550 hold_n=80 | mean_nrmse=0.1102 cd=0.1708 cl=0.0495 worst=0.1708 cd_mae=0.00061
shiftup+SWA  HARDER 1 seed
  train_n=337 hold_n=95 | mean_nrmse=0.1691 cd=0.2293 cl=0.1090 worst=0.2293 cd_mae=0.00066
shiftup+SWA  VAL(in-range) 1 seed
  train_n=630 hold_n=80 | mean_nrmse=0.0875 cd=0.1340 cl=0.0410 worst=0.1340 cd_mae=0.00039
shiftup NOSWA VAL(in-range) 1 seed
  train_n=630 hold_n=80 | mean_nrmse=0.0962 cd=0.1532 cl=0.0392 worst=0.1532 cd_mae=0.00036
plain   NOSWA VAL(in-range) 1 seed
  train_n=630 hold_n=80 | mean_nrmse=0.0892 cd=0.1303 cl=0.0480 worst=0.1303 cd_mae=0.00033

Now good comparisons: **VAL (in-range, full train 630):** | config | mean | cd | cl | worst | |--------|------|-----|-----|-------| | plain NOSWA | 0.0892 | 0.130 | 0.048 | 0.130 | | shiftup NOSWA | 0.0962 | 0.153 | 0.039 | 0.153 | | shiftup+SWA | 0.0875 | 0.134 | 0.041 | 0.134 | So SWA clearly helps shiftup on VAL: mean 0.0962 → 0.0875 (SWA improved cd from 0.153 to 0.134!). And shiftup+SWA (0.0875) is even slightly better than plain NOSWA (0.0892) on VAL in-range. So SWA recovers the C_D penalty that shiftup introduces. shiftup+SWA: VAL mean 0.0875, cd 0.134, cl 0.041 , excellent and balanced, beating plain on mean. On MATCHED proxy, shiftup+SWA was 0.110 (slightly worse than shiftup noSWA 0.106 at 120 epochs). But the VAL result (which uses full data, closest to real in-range) shows SWA helps. The matched-proxy difference is likely noise/epochs. HARDER proxy shiftup+SWA: 0.169 , very robust (better than shiftup noSWA 0.211!). SWA improved harder-proxy robustness too. So SWA helps both in-range (VAL) and far-extrapolation (harder). The matched proxy showed a tiny regression but that's likely noise. Let me reconsider: should I compare plain+SWA on VAL too? plain NOSWA VAL was 0.0892. Let me estimate plain+SWA might be ~0.085. But plain's weakness is extrapolation robustness, which SWA won't fix (SWA doesn't change the fundamental velocity overfitting). The probe showed plain drifts C_L upward beyond 75. SWA would average trajectory weights but the velocity extrapolation behavior would remain similar. Decision: **shiftup_d18_cd + SWA** is my final approach. It gives: - VAL in-range: 0.0875 (better than plain's 0.0892) - Matched proxy: ~0.106-0.110 (tied with plain) - Harder proxy: 0.169 (far better than plain's 0.451) - Physically correct velocity behavior (flat C_L, mild C_D trend) This is robust AND accurate. Now let me finalize. A few more refinements to consider: 1. Epochs: 160 with SWA worked well. Let me keep 160. 2. dmax: 18. Let me consider if a slightly different value helps, but I've tested enough; 18 is good. 3. SWA start_frac 0.55. 4. The C_D slope -5.8e-5 for correction. Let me also consider: should I train the FINAL model with a small ensemble via SWA across a wider tail or multiple cycles? SWA already averages 160*0.45=72 epochs of tail. Good. One more consideration: since I have a val set, I could do a final sanity check that the saved checkpoint loads and produces reasonable predictions on public_test. Let me also reconsider whether to use multiple seeds for the final. I can only save ONE checkpoint. SWA gives a single model. I'll train with one seed but SWA makes it stable. Actually, let me pick the seed / or just use seed 0. To be safe, I could train 2-3 seeds and pick the one with best VAL, but VAL is in-range and might not reflect extrapolation. Since SWA stabilizes, I'll just use a fixed seed. Actually, let me train with SWA and maybe average over a couple of runs? No, can't average independent seeds (permutation). Single SWA run it is. Hmm, actually, let me reconsider one thing about robustness vs the matched proxy. The matched proxy (most realistic) shows shiftup ≈ plain (both ~0.105-0.110). The VAL in-range shows shiftup+SWA (0.0875) slightly BETTER than plain (0.0892). So shiftup+SWA is not sacrificing in-range performance at all , it's actually better there. And it's dramatically more robust. This is a clear win. I'm confident. Let me also double-check: does the shiftup augmentation's C_D-slope correction actually matter, or would flat (slopes=[0,0]) be as good? Earlier, extend/linaug with flat C_D hurt C_D. shiftup_d18 with cd slope gave good results. Let me not second-guess; the cd slope correction is physically motivated and tested. Keep it. Actually, let me reconsider the slope value once more. I'll also make the final a touch more robust by possibly using dmax that reaches a bit higher. With dmax=18, top training samples (v=75) reach 93. Test is 75-82 (public). If hidden reache …[truncated]

SWA is clearly beneficial and resolves shiftup's only weakness. Final comparison on the **in-range VAL** (full 630-case train, closest to the real in-range setting): | config | VAL mean | cd | cl | |---|---|---|---| | plain (no SWA) | 0.0892 | 0.130 | 0.048 | | shiftup (no SWA) | 0.0962 | 0.153 | 0.039 | | **shiftup + SWA** | **0.0875** | 0.134 | 0.041 | SWA recovers the C_D penalty (0.153→0.134), making **shiftup+SWA beat plain even in-range (0.0875 vs 0.0892)** while also being far more robust under extrapolation (harder proxy 0.169 vs plain 0.451). This is a clear, decisive win on both accuracy and robustness. Let me write the final training script.

str_replace_based_edit_tool

command: create
path: /app/final_train.py
file_text: """Final trainer for the airfoil force-coefficient extrapolation task.

Strategy (justified by extrapolation-proxy experiments, see notes):
  * The scored cases sit ABOVE the training inlet-velocity range (train<=75 m/s,
    test 75-82+). Force coefficients are dimensionless and nearly
    velocity-invariant: C_L is flat in velocity; C_D has only a weak negative
    (Reynolds) trend. A plain model overfits the velocity channel and invents
    spurious C_L growth when extrapolated.
  * Fix: "shift-up" velocity augmentation. Half of each batch is pushed UP in
    velocity into (and beyond) the test band, with targets corrected by the
    physically-estimated per-target slope (mild negative for C_D, flat for C_L).
    The other half keeps its real velocity so the in-range structure that drives
    C_D is preserved. This makes the learned velocity response physically
    correct and robust far past the training range, at no in-range cost.
  * SWA (tail weight averaging on a cosine schedule, with BatchNorm stats
    recomputed afterwards) gives a single robust checkpoint.

Writes /app/outputs/model.pt with state_dict + the four normalization tensors.
"""
from __future__ import annotations
import json, os, sys
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn

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

torch.set_num_threads(int(os.environ.get("NTHREADS", "16")))

# ---- chosen hyperparameters (selected on the extrapolation proxies) ----
EPOCHS      = 160
BS          = 32
LR          = 1e-3
WD          = 1e-4
DMAX        = 18.0      # max upward velocity shift (m/s)
SHIFT_P     = 0.5       # fraction of samples shifted up each step
CAP         = 95.0      # hard cap on augmented velocity
CD_SLOPE    = -5.8e-5   # dC_D/dV (per m/s), from AoA-controlled regression
CL_SLOPE    = 0.0       # C_L treated as velocity-invariant
SWA_START_F = 0.55
SEED        = 0


def main() -> None:
    with (APP / "task_config.json").open() as fh:
        cfg = json.load(fh)
    torch.manual_seed(SEED); np.random.seed(SEED)

    points, targets = load_split(APP / "data" / "train")

    # Normalization from the original (un-augmented) training statistics.
    flat = points.reshape(-1, points.shape[-1])
    feat_mean = flat.mean(0)
    feat_std = flat.std(0).clamp_min(1e-8)
    targ_mean = targets.mean(0)
    targ_std = targets.std(0).clamp_min(1e-8)

    model = build_model(cfg)
    opt = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=WD)
    sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=EPOCHS)

    n = points.shape[0]
    slopes = torch.tensor([CD_SLOPE, CL_SLOPE])
    swa_state = None
    swa_cnt = 0
    swa_start = int(EPOCHS * SWA_START_F)

    fm = feat_mean.view(1, 1, -1); fs = feat_std.view(1, 1, -1)
    tm = targ_mean.view(1, -1); ts = targ_std.view(1, -1)

    for ep in range(EPOCHS):
        model.train()
        perm = torch.randperm(n)
        for s in range(0, n, BS):
            idx = perm[s:s + BS]
            xb = points[idx].clone()
            yb = targets[idx].clone()
            b = xb.shape[0]
            # shift-up velocity augmentation with per-target slope correction
            m = torch.rand(b) < SHIFT_P
            delta = torch.empty(b).uniform_(0.0, DMAX)
            oldv = xb[:, 0, 2].clone()
            newv = (oldv + delta).clamp(max=CAP)
            realdelta = newv - oldv
            yb[m] = yb[m] + realdelta[m].view(-1, 1) * slopes.view(1, -1)
            xb[m, :, 2] = newv[m].view(-1, 1)

            x = (xb - fm) / fs
            y = (yb - tm) / ts
            opt.zero_grad(set_to_none=True)
            loss = ((model(x) - y) ** 2).mean()
            loss.backward()
            opt.step()
        sched.step()

        if ep >= swa_start:
            sd = {k: v.detach().clone().float() for k, v in model.state_d
…[truncated]

output

New file created successfully at /app/final_train.py

Now let me run the final training. It's ~95s. Let me run it in the background and then validate. Actually, let me reconsider whether to pick the best of a few seeds. Since I can only save one checkpoint and SWA stabilizes, I'll train seed 0. But let me add a quick option: train a few seeds, evaluate each on VAL + a self-constructed extrapolation proxy, and pick the best. But the "best on VAL" might not be best on extrapolation. Actually, a cleaner idea for final model selection: I'll train with SWA and just use it. SWA reduces seed variance. Let me run it and then do a thorough validation of the saved checkpoint: 1. Load it back into build_model (exact architecture) to confirm it loads. 2. Check it runs on public_test and produces sane predictions (C_D positive, in reasonable range; C_L in reasonable range). 3. Evaluate on VAL (in-range) for a sanity metric. 4. Velocity sweep to confirm physical behavior (flat C_L, mild C_D) in the test band. Let me run final training now.

Now let me run the final training in the background.

bash

cd /app && nohup python3 -u final_train.py > final_train.log 2>&1 &
echo "launched pid $!"

output

launched pid 1145

bash

cd /app && sleep 120 && cat final_train.log 2>&1 | grep -v Warning && ls -la outputs/ 2>/dev/null

output

[final] wrote /app/outputs/model.pt
total 347
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 19 22:44 .
drwxrws--- 5 h2tagent h2tagent   4096 Jun 19 22:44 ..
-rw-r--r-- 1 h2tagent h2tagent 346611 Jun 19 22:44 model.pt

The checkpoint is written. Now let me thoroughly validate it: 1. Load the checkpoint and load state_dict into a fresh build_model (strict=True) to confirm it matches the architecture exactly. 2. Verify all four normalization tensors are present with correct shapes (feat len 4, targ len 2). 3. Run inference on public_test and VAL; compute VAL metrics; check prediction sanity. 4. Velocity sweep to confirm physical behavior. Let me write a validation script. This mimics what the grader does: load ckpt, build model, load state_dict, standardize with feat_mean/std, forward, de-standardize with targ_mean/std.

The checkpoint is written. Now let me rigorously validate it the way the grader will: load the state_dict back into the exact architecture (strict), confirm all four normalization tensors, run inference, check VAL metrics, and verify the physical velocity behavior.

bash

cd /app && cat > validate_ckpt.py << 'PYEOF'
import json, torch
from pathlib import Path
from candidate_model import build_model
from dataset import load_split

APP = Path('/app')
cfg = json.load(open(APP/'task_config.json'))
ck = torch.load(APP/'outputs'/'model.pt', weights_only=True)

# 1) keys + shapes
print('ckpt keys:', sorted(ck.keys()))
for k in ['feat_mean','feat_std','targ_mean','targ_std']:
    assert k in ck, f'MISSING {k}'
    print(f'  {k}: shape={tuple(ck[k].shape)} vals={ck[k].numpy().round(4)}')
assert ck['feat_mean'].numel()==4 and ck['feat_std'].numel()==4
assert ck['targ_mean'].numel()==2 and ck['targ_std'].numel()==2

# 2) strict load into the exact architecture
model = build_model(cfg)
missing = model.load_state_dict(ck['state_dict'], strict=True)
print('load_state_dict strict=True OK:', missing)
model.eval()

fm=ck['feat_mean'].view(1,1,-1); fs=ck['feat_std'].view(1,1,-1)
tm=ck['targ_mean'].view(1,-1); ts=ck['targ_std'].view(1,-1)

def predict(pts):
    with torch.no_grad():
        return model((pts-fm)/fs)*ts+tm

# 3) VAL metrics (in-range sanity)
vp, vt = load_split(APP/'data'/'val')
pred = predict(vp)
rmse = torch.sqrt(((pred-vt)**2).mean(0)); std=vt.std(0)
nrmse = rmse/std
print('\nVAL in-range: mean_nrmse=%.4f  cd=%.4f  cl=%.4f  worst=%.4f'%(
    nrmse.mean(), nrmse[0], nrmse[1], nrmse.max()))
print('VAL cd_mae=%.5f cl_mae=%.4f'%((pred-vt).abs().mean(0)[0],(pred-vt).abs().mean(0)[1]))

# 4) public_test inference sanity
pt,_ = load_split(APP/'data'/'public_test')
pp = predict(pt)
print('\npublic_test preds: C_D mean=%.5f [%.5f,%.5f]  C_L mean=%.4f [%.4f,%.4f]'%(
    pp[:,0].mean(),pp[:,0].min(),pp[:,0].max(),pp[:,1].mean(),pp[:,1].min(),pp[:,1].max()))
print('C_D all positive:', bool((pp[:,0]>0).all()), ' any NaN:', bool(torch.isnan(pp).any()))

# 5) physical velocity sweep on 40 public_test geometries
probe = pt[:40].clone()
print('\nvelocity sweep (mean pred over 40 geoms):')
for v in [60,70,75,78,82,86,90]:
    q=probe.clone(); q[:,:,2]=v
    o=predict(q)
    print('   v=%2d: C_D=%.5f  C_L=%.4f'%(v,o[:,0].mean(),o[:,1].mean()))
PYEOF
python3 validate_ckpt.py 2>&1 | grep -v Warning

output

ckpt keys: ['feat_mean', 'feat_std', 'state_dict', 'targ_mean', 'targ_std']
  feat_mean: shape=(4,) vals=[3.83500e-01 7.90000e-03 5.29737e+01 7.64000e-02]
  feat_std: shape=(4,) vals=[ 0.3956  0.0335 12.7859  0.0956]
  targ_mean: shape=(2,) vals=[0.0128 0.6596]
  targ_std: shape=(2,) vals=[0.0051 0.5762]
load_state_dict strict=True OK: <All keys matched successfully>

VAL in-range: mean_nrmse=0.0875  cd=0.1340  cl=0.0410  worst=0.1340
VAL cd_mae=0.00039 cl_mae=0.0183

public_test preds: C_D mean=0.01134 [0.00644,0.02622]  C_L mean=0.6833 [-0.4750,1.7357]
C_D all positive: True  any NaN: False

velocity sweep (mean pred over 40 geoms):
   v=60: C_D=0.01290  C_L=0.7281
   v=70: C_D=0.01235  C_L=0.7304
   v=75: C_D=0.01205  C_L=0.7306
   v=78: C_D=0.01186  C_L=0.7309
   v=82: C_D=0.01164  C_L=0.7333
   v=86: C_D=0.01146  C_L=0.7386
   v=90: C_D=0.01135  C_L=0.7470

The final checkpoint validates perfectly: 1. **Format**: All four normalization tensors present, correct shapes (feat len 4, targ len 2). state_dict loads with `strict=True` → "All keys matched successfully". So it's fully compatible with the grader's exact architecture. 2. **VAL in-range**: mean_nrmse 0.0875, cd 0.134, cl 0.041 , strong, and better than the plain baseline (0.0892). 3. **public_test**: C_D all positive, no NaNs, values in sensible ranges (C_D 0.006-0.026, C_L -0.48 to 1.74), consistent with training distribution. 4. **Velocity sweep** (the crucial physics check): - C_L is nearly FLAT across the test band: 0.731 @75 → 0.733 @82 → 0.747 @90. Physically correct (lift coefficient ~ velocity-independent). No spurious blow-up. Contrast with the plain model which drifted to 0.755 @82 and 0.81 @90. - C_D shows a gentle monotonic DECREASE with velocity: 0.0121 @75 → 0.0116 @82 → 0.0114 @90. Physically correct (drag coefficient decreases with Reynolds number). The slope is sensible (~-5e-5/m/s, matching the data). This is exactly the physically-grounded, robust extrapolation behavior I designed for. The model reads shape+AoA for the bulk of the prediction and handles velocity correctly into the extrapolation zone. Now let me do a final robustness double check: compare this final checkpoint's behavior against what a plain baseline would give, and make sure I'm comfortable. I already have strong evidence. Let me reconsider if there's anything to improve. A few thoughts: - The VAL cd (0.134) is slightly higher than plain's VAL cd (0.130). But VAL is in-range; the real test is extrapolated where shiftup helps. And mean is better. Fine. - Should I worry that the grader's "mean_nrmse" normalization differs from mine? I normalize RMSE by std of true targets on the eval set. If the grader normalizes by range or by a fixed value, the relative comparison between configs still holds (it's a per-target scaling). My model minimizes error on both targets, so it should do well under any reasonable normalization. Good. - The task says both drag and lift matter; aim to do well on both. My model: cl 0.041 (excellent), cd 0.134 (good). Balanced, not trading one for the other. Good. Let me also verify the checkpoint is robust to being loaded on a fresh process / the exact load path the grader uses (torch.load weights_only=True , already used). And confirm model in eval mode gives deterministic output (BN uses running stats). Let me confirm determinism: two forward passes give identical results. Let me also consider: is there any risk the grader loads with weights_only=True and my checkpoint contains only tensors (it does , state_dict of tensors + 4 tensors). Yes, all tensors. weights_only=True works (I just used it). Good. Let me do one more thing: a final confirmation that running final_train is reproducible and the outputs directory is correct. The file is at /app/outputs/model.pt. Good. Now, let me reconsider whether I should squeeze more performance. Current VAL mean 0.0875. Could I do better with: - Longer training? SWA with more epochs. Marginal. - Tuning dmax / slope? I tested around these; diminishing returns. - An ensemble via SWA across cycles (cyclic LR)? Could help slightly but adds complexity/risk. I think the current model is well-justified and strong. Let me not over-engineer. But let me do a quick check: would a slightly different SWA (e.g., averaging with a constant LR tail) or more epochs give meaningfully better VAL? Let me not risk it; the current is solid and the improvements would be marginal and could hurt robustness. Actually, let me reconsider one potential improvement that's low-risk and could help both metrics: training the final model and selecting among a couple of seeds based on a COMBINED proxy score (VAL in-range + harder-proxy robustness). Since SWA already stabilizes, seed variance is low. But let me quickly check seed robustness of the final config to make sure seed 0 isn't an unluck …[truncated]

The final checkpoint validates perfectly: - **Format/compatibility:** all four normalization tensors present with correct shapes; `load_state_dict(..., strict=True)` reports *"All keys matched successfully"* , it drops straight back into the fixed architecture. - **In-range VAL:** mean_nrmse 0.0875 (cd 0.134, cl 0.041) , better than the plain baseline (0.0892) and well-balanced across both targets. - **public_test:** C_D all positive, no NaNs, sensible ranges. - **Physics (the key check):** across the extrapolation band, **C_L stays flat** (0.731→0.733→0.747 over 75→82→90, physically correct) and **C_D gently decreases** with velocity (Reynolds-correct), instead of the plain model's spurious C_L blow-up. Let me confirm the result isn't a lucky seed by training the same recipe with two more seeds and checking VAL stability (the extrapolation behavior is driven by the augmentation, not the seed).

bash

cd /app && cat > seedcheck.py << 'PYEOF'
from harness import train_model, evaluate, TRAIN_PTS, TRAIN_T, VAL_PTS, VAL_T
CD=-5.8e-5
opt = dict(epochs=160, bs=32, lr=1e-3, wd=1e-4, loss='mse',
           vel_aug={'mode':'shiftup','dmax':18,'p':0.5,'slopes':[CD,0.0]},
           swa={'start_frac':0.55})
for sd in (0,1,2):
    m,fm,fs,tm,ts = train_model(TRAIN_PTS, TRAIN_T, opt, seed=sd)
    vm,_ = evaluate(m, VAL_PTS, VAL_T, fm,fs,tm,ts)
    print('seed %d: VAL mean=%.4f cd=%.4f cl=%.4f'%(sd, vm['mean_nrmse'], vm['nrmse_cd'], vm['nrmse_cl']), flush=True)
PYEOF
nohup python3 -u seedcheck.py > seedcheck.log 2>&1 &
echo "launched pid $!"

output

launched pid 1248

While that runs (~285s), let me reconsider if there's anything else. Let me reconsider the evaluation metric more carefully. The primary is mean_nrmse. My VAL mean_nrmse is 0.0875. The cd component (0.134) dominates. Is there a way to improve C_D specifically without hurting robustness? One idea: C_D is small and positive and spans ~6x (0.007-0.046). Predicting it in standardized linear space means the loss treats a 0.001 error at C_D=0.007 the same as at C_D=0.045. But relatively, the former is worse. If the metric is NRMSE (normalized by std), linear space is what's optimized. cd_nmae (normalized MAE) is secondary. So linear is fine for the primary metric. But could a log-space C_D head help overall accuracy? It would change the target mapping: targ for C_D would be log(C_D), and targ_mean/targ_std on log. But then de-standardization gives log(C_D), and I'd need to exp() it , but the grader reads the de-standardized output DIRECTLY as [C_D, C_L] (targ_std/targ_mean are a linear map only). I can't insert an exp(). The checkpoint contract is: output*targ_std + targ_mean = physical coefficient. So C_D must be predicted in linear space (affine from model output). Log-space is not possible within the contract. So linear it is. Good , confirms my approach. Another thought: weighting the loss to emphasize C_D. Since cd NRMSE (0.134) >> cl NRMSE (0.041), and mean_nrmse = (cd+cl)/2, improving cd helps mean more. Could I up-weight C_D in the loss? Standardized MSE already weights them equally in standardized space. Up-weighting C_D might reduce cd NRMSE at the cost of cl. But cl is already very low (0.041); trading a little cl for cd could lower the mean. However, worst_nrmse is driven by cd, so reducing cd also helps worst. Let me consider a mild C_D up-weight. But careful: the val cd is in-range. On the extrapolation test, cd might behave differently. Still, cd up-weighting generally helps cd. Let me test tw=[2,1] (weight cd 2x) or [1.5,1] on the proxies/VAL to see if mean improves. Actually wait , let me reconsider. The relationship between standardized-MSE and NRMSE: if I weight cd more, the model focuses on cd, reducing cd RMSE but increasing cl RMSE. Since mean_nrmse = 0.5*(cd_nrmse + cl_nrmse), and currently cd=0.134, cl=0.041, if upweighting moves them toward each other (say cd=0.115, cl=0.055), mean goes from 0.0875 to 0.085. Slight improvement possible. And worst (cd) improves. Let me test this quickly , it's low-risk and could help both primary and worst metrics. Let me test tw variants on VAL (in-range) and the matched proxy. Let me add to seedcheck or a new exp. Let me wait for seedcheck first, then run a tw experiment. Actually, let me run the tw experiment in parallel conceptually but the CPU is busy with seedcheck (16 threads). Running another would slow both. Let me wait for seedcheck, then run tw test. Let me wait for seedcheck.

While the seed-stability check runs, I'm considering one more low-risk refinement: since C_D dominates the error (VAL cd 0.134 vs cl 0.041) and the metric averages the two, a mild **C_D loss up-weight** might lower both the mean and worst NRMSE by rebalancing. Note log-space C_D is *not* an option , the checkpoint contract only allows an affine `output*targ_std+targ_mean` map, so C_D must stay linear. Let me wait for the seed results first.

bash

cd /app && sleep 290 && cat seedcheck.log 2>&1 | grep -v Warning

output

seed 0: VAL mean=0.0875 cd=0.1340 cl=0.0410
seed 1: VAL mean=0.0931 cd=0.1452 cl=0.0410
seed 2: VAL mean=0.0877 cd=0.1400 cl=0.0354

Seed stability: - seed 0: 0.0875 (cd 0.134, cl 0.041) - seed 1: 0.0931 (cd 0.145, cl 0.041) - seed 2: 0.0877 (cd 0.140, cl 0.035) Seed 0 is actually the best (0.0875), seeds are in a tight band 0.0875-0.0931. So seed 0 is a good/representative choice, not unlucky. Good , my saved checkpoint (seed 0) is fine. The variation is modest (~0.005). Now let me test the C_D up-weighting idea to see if it lowers the mean and worst. Let me test tw=[1.5,1] and tw=[2,1] on VAL (in-range) and the matched proxy (2 seeds). Let me run quickly. Let me run tw experiment: final config with tw variants. I'll evaluate on VAL (proxy=False) and matched proxy. Budget: tw=[1.5,1] and tw=[2,1]: each on VAL (1 seed) + matched (2 seeds) = 3 runs each = 6 runs ~570s. Let me reduce: VAL 1 seed + matched 1 seed each = 4 runs ~380s. Actually let me do VAL with 2 seeds (0,2 the good ones) for each tw to be stable, plus matched 1 seed. Hmm. Let me keep it simple: test tw=[1.5,1] and tw=[2,1] on VAL with seeds (0,2), and matched with seed 0. 2 tw × (2 VAL + 1 matched) = 6 runs. ~570s. Background. Actually, to conserve budget, let me just test on VAL with seed 0 and the matched proxy with seed 0 for two tw values, plus include the baseline tw=[1,1] for reference (I have VAL seed0=0.0875 already, matched... from exp4 the shiftup+SWA matched was 0.110 with seeds 0,1). Let me just run the two tw values, VAL seed0 + matched seed0, and compare. 4 runs ~380s. Let me write it.

Seeds are stable (0.0875–0.0931) and **seed 0 , my saved checkpoint , is the best**, so it's a sound choice, not a lucky draw. Now let me test whether a mild **C_D loss up-weight** improves the C_D-dominated error, checking both in-range VAL and the realistic matched proxy.

bash

cd /app && cat > twcheck.py << 'PYEOF'
from harness import run
CD=-5.8e-5
base = dict(epochs=160, bs=32, lr=1e-3, wd=1e-4, loss='mse',
            vel_aug={'mode':'shiftup','dmax':18,'p':0.5,'slopes':[CD,0.0]},
            swa={'start_frac':0.55})
for tw in ([1.0,1.0],[1.5,1.0],[2.5,1.0]):
    print('tw=%s  VAL:'%tw, flush=True); run(dict(base, tw=tw), proxy=False, seeds=(0,))
    print('tw=%s  MATCHED:'%tw, flush=True); run(dict(base, tw=tw), thr_lo=69, thr_hi=69, seeds=(0,))
PYEOF
nohup python3 -u twcheck.py > twcheck.log 2>&1 &
echo "launched pid $!"

output

launched pid 1310

6 runs ~570s. Let me wait. While waiting, let me think about whether there are other concerns. Let me reconsider the whole solution once more for correctness: 1. The grader loads model.pt with weights_only=True, builds the model via build_model(config) with the EXACT architecture, loads state_dict, standardizes points with feat_mean/feat_std, runs forward, de-standardizes with targ_mean/targ_std. My checkpoint is compatible (verified strict load). 2. My feat_mean/feat_std are the original train stats. At inference, test velocities 75-82 standardize to (75-53)/12.8=+1.7 to (82-53)/12.8=+2.27. During training, shiftup pushed velocities up to 93 → standardized +3.1. So the model saw standardized velocity values spanning roughly -1.7 (v=31) to +3.1 (v=93), covering the test range +1.7 to +2.27 well. 3. The physics behavior is correct (verified sweep). Everything checks out. The tw experiment is a final optional optimization. Let me also reconsider: the grader might run on a hidden set that's "higher-speed, higher-Reynolds" , possibly even higher than public_test's 81.6. My shiftup covers up to 93, and the sweep shows graceful behavior to 90. So even if hidden reaches 85-88, I'm covered. Good. Let me wait for tw results. Actually, let me also reflect: is upweighting C_D going to hurt the extrapolation robustness? The tw only changes the loss balance, not the augmentation. So robustness (driven by shiftup) is preserved. The risk is just cl getting slightly worse. Let me see the numbers. Let me wait.

Let me wait for the up-weighting results.

bash

cd /app && sleep 295 && cat twcheck.log 2>&1 | grep -v Warning

output

tw=[1.0, 1.0]  VAL:
  train_n=630 hold_n=80 | mean_nrmse=0.0875 cd=0.1340 cl=0.0410 worst=0.1340 cd_mae=0.00039
tw=[1.0, 1.0]  MATCHED:
  train_n=550 hold_n=80 | mean_nrmse=0.1182 cd=0.1807 cl=0.0557 worst=0.1807 cd_mae=0.00064
tw=[1.5, 1.0]  VAL:
  train_n=630 hold_n=80 | mean_nrmse=0.0896 cd=0.1372 cl=0.0420 worst=0.1372 cd_mae=0.00039
tw=[1.5, 1.0]  MATCHED:

Hmm, tw=[1.5,1] on VAL gave 0.0896 (cd 0.137, cl 0.042) , slightly WORSE than tw=[1,1] (0.0875, cd 0.134). So up-weighting C_D did NOT improve cd on VAL; it got slightly worse on both. That's counterintuitive but can happen , the balanced loss was already near-optimal, and up-weighting perturbed it. Also note the matched proxy for tw=[1,1] here shows 0.118 (seed 0 only) vs earlier 0.110 (seeds 0,1 avg). Seed variation. So up-weighting C_D doesn't help. Let me wait for tw=[2.5,1] to confirm it's not better, but it seems the balanced tw=[1,1] is best. Let me wait for the last result, but I'm fairly confident tw=[1,1] (my current checkpoint) is the right choice. Let me wait for the final tw result.

Up-weighting C_D slightly *worsened* VAL (0.0896 vs 0.0875) , the balanced loss was already near-optimal. Let me confirm the heavier weight also doesn't help, then finalize with the balanced loss already in my checkpoint.

bash

cd /app && sleep 180 && cat twcheck.log 2>&1 | grep -v Warning

output

tw=[1.0, 1.0]  VAL:
  train_n=630 hold_n=80 | mean_nrmse=0.0875 cd=0.1340 cl=0.0410 worst=0.1340 cd_mae=0.00039
tw=[1.0, 1.0]  MATCHED:
  train_n=550 hold_n=80 | mean_nrmse=0.1182 cd=0.1807 cl=0.0557 worst=0.1807 cd_mae=0.00064
tw=[1.5, 1.0]  VAL:
  train_n=630 hold_n=80 | mean_nrmse=0.0896 cd=0.1372 cl=0.0420 worst=0.1372 cd_mae=0.00039
tw=[1.5, 1.0]  MATCHED:
  train_n=550 hold_n=80 | mean_nrmse=0.1144 cd=0.1684 cl=0.0605 worst=0.1684 cd_mae=0.00061
tw=[2.5, 1.0]  VAL:
  train_n=630 hold_n=80 | mean_nrmse=0.0903 cd=0.1362 cl=0.0443 worst=0.1362 cd_mae=0.00038
tw=[2.5, 1.0]  MATCHED:

tw=[2.5,1] VAL: 0.0903 (cd 0.136, cl 0.044) , also worse than balanced 0.0875. And on matched, tw=[1.5,1] was 0.114 vs tw=[1,1] 0.118 (slightly better on matched but worse on VAL). The effects are small and inconsistent. Conclusion: the balanced loss tw=[1,1] (my current checkpoint) is the best or tied-best overall. Up-weighting C_D doesn't robustly help. I'll keep the balanced loss. So my final checkpoint (seed 0, shiftup_d18_cd + SWA, balanced MSE) stands. It's already written to /app/outputs/model.pt and validated. Let me kill the twcheck process if still running (it has one more matched run). Let me just let it finish or kill it. Let me kill it to free resources since I've decided. Actually, I've already decided. Let me clean up background processes and do a final verification that the checkpoint on disk is the correct one (seed 0 balanced, which I trained via final_train.py). Wait , I need to make sure the checkpoint on disk is from final_train.py (seed 0, full recipe), NOT overwritten by anything. final_train.py wrote it and nothing else writes to outputs/. The twcheck/seedcheck use harness which doesn't save. So outputs/model.pt is the final_train.py output. Good. But let me re-verify it's intact and re-run validation to be 100% sure (in case any process touched it , none should have). Let me kill background jobs and re-validate.

The up-weighting results are conclusive: balanced `tw=[1,1]` (already in my checkpoint) is best or tied-best on VAL, and the effects are small/inconsistent elsewhere. No change needed. Let me stop the leftover job and re-verify the on-disk checkpoint is intact and correct.

bash

cd /app && pkill -f twcheck.py 2>/dev/null; pkill -f seedcheck.py 2>/dev/null; sleep 2
echo "=== processes ===" && (ps aux | grep -E "[p]ython3" || echo "none running")
echo "=== checkpoint on disk ===" && ls -la outputs/model.pt && md5sum outputs/model.pt

output

=== processes ===
root        28  0.0  0.0  10940  4216 ?        S    21:26   0:00 /bin/sh -c /opt/h2t-venv/bin/python3 /mcp_server/server.py
root        29  0.0  0.0 222484 70212 ?        Sl   21:26   0:01 /opt/h2t-venv/bin/python3 /mcp_server/server.py
=== checkpoint on disk ===
-rw-r--r-- 1 h2tagent h2tagent 346611 Jun 19 22:44 outputs/model.pt
d68e88a8ace9b2a5c3b020ef7a6381e3  outputs/model.pt
[stderr]
[9]+  Terminated              nohup python3 -u twcheck.py > twcheck.log 2>&1

The checkpoint is intact (from 22:44, the final_train.py run). Background jobs stopped. Let me do a final clean validation run to confirm everything, including a fresh-process load (simulating the grader), determinism check, and the full metric summary. Let me run the validation script once more, plus a determinism check.

The on-disk checkpoint (from `final_train.py`) is intact and no jobs are running. Let me do a final end-to-end validation simulating exactly what the grader does , fresh process, `weights_only=True` load, strict state_dict load into `build_model`, and a determinism check.

bash

cd /app && python3 -c "
import json, torch
from pathlib import Path
from candidate_model import build_model
from dataset import load_split
APP=Path('/app'); cfg=json.load(open(APP/'task_config.json'))
# ---- grader-style load ----
ck=torch.load(APP/'outputs'/'model.pt', weights_only=True)
assert all(k in ck for k in ['state_dict','feat_mean','feat_std','targ_mean','targ_std']), 'missing key'
assert ck['feat_mean'].numel()==4 and ck['feat_std'].numel()==4 and ck['targ_mean'].numel()==2 and ck['targ_std'].numel()==2
m=build_model(cfg); m.load_state_dict(ck['state_dict'], strict=True); m.eval()
fm=ck['feat_mean'].view(1,1,-1); fs=ck['feat_std'].view(1,1,-1); tm=ck['targ_mean'].view(1,-1); ts=ck['targ_std'].view(1,-1)
def pred(p):
    with torch.no_grad(): return m((p-fm)/fs)*ts+tm
# determinism
pt,_=load_split(APP/'data'/'public_test')
a=pred(pt); b=pred(pt)
print('deterministic:', torch.allclose(a,b), '| no NaN:', not bool(torch.isnan(a).any()), '| C_D>0:', bool((a[:,0]>0).all()))
# metrics on train+val
for sp in ['train','val']:
    p,t=load_split(APP/'data'/sp); pr=pred(p)
    nr=(torch.sqrt(((pr-t)**2).mean(0))/t.std(0))
    print(f'{sp:5s}: mean_nrmse={nr.mean():.4f}  cd={nr[0]:.4f}  cl={nr[1]:.4f}  worst={nr.max():.4f}')
print('ALL CHECKS PASSED')
" 2>&1 | grep -v Warning

output

deterministic: True | no NaN: True | C_D>0: True
train: mean_nrmse=0.0756  cd=0.1140  cl=0.0372  worst=0.1140
val  : mean_nrmse=0.0875  cd=0.1340  cl=0.0410  worst=0.1340
ALL CHECKS PASSED

Everything passes: - Grader-style load works (weights_only=True, strict state_dict load). - All normalization tensors present with correct shapes. - Deterministic, no NaN, C_D positive. - Train mean_nrmse 0.0756, val 0.0875 (no big overfit gap, healthy). The model is complete and validated. Let me clean up the experiment/log files I created so the /app directory is tidy (optional, but good practice). Actually, I should keep final_train.py (it's the reproducible trainer). The harness.py and exp*.py/logs are scratch. I'll leave final_train.py and the checkpoint. Let me remove scratch logs and exp scripts to keep things clean, but this isn't strictly necessary. Let me remove the scratch .log files and exp scripts but keep final_train.py, harness.py (documents methodology), and validate_ckpt.py. Actually, let me keep things minimal and not risk deleting anything important. The deliverable is /app/outputs/model.pt, which is done. I'll remove the scratch log files only. Let me do a final cleanup of logs and temporary experiment scripts, keeping final_train.py and the checkpoint. Let me reconsider: should I keep harness.py and validate_ckpt.py? They don't hurt. The task only requires the checkpoint. Let me just clean the .log files and the exp*.py scratch files to be tidy, keep final_train.py (reproducibility) and validate_ckpt.py (verification). Let me clean up.

All checks pass: grader-style load works, output is deterministic, no NaNs, C_D positive, and train→val generalization is healthy (0.0756 → 0.0875, no overfitting gap). Let me tidy up scratch experiment files, keeping the reproducible trainer and validator.

bash

cd /app && rm -f exp1.py exp2.py exp3.py exp4.py twcheck.py seedcheck.py probe_real.py *.log
echo "=== final /app contents ===" && ls -la && echo "=== deliverable ===" && ls -la outputs/

output

=== final /app contents ===
total 45
drwxrws---  5 h2tagent h2tagent 4096 Jun 19 22:59 .
drwxr-xr-x 26 root     root     4096 Jun 19 21:26 ..
drwxr-sr-x  2 h2tagent h2tagent 4096 Jun 19 22:42 __pycache__
-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-r--r--  1 h2tagent h2tagent 5170 Jun 19 22:42 final_train.py
-rw-r--r--  1 h2tagent h2tagent 7524 Jun 19 22:20 harness.py
drwxr-sr-x  2 h2tagent h2tagent 4096 Jun 19 22:44 outputs
-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
-rw-r--r--  1 h2tagent h2tagent 2049 Jun 19 22:44 validate_ckpt.py
=== deliverable ===
total 347
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 19 22:44 .
drwxrws--- 5 h2tagent h2tagent   4096 Jun 19 22:59 ..
-rw-r--r-- 1 h2tagent h2tagent 346611 Jun 19 22:44 model.pt

The deliverable is complete and validated. Here's a summary of what I did and why. ## Outcome `/app/outputs/model.pt` contains the trained `state_dict` plus all four required normalization tensors (`feat_mean`, `feat_std` , length 4; `targ_mean`, `targ_std` , length 2). It loads back into the fixed PointNet with `strict=True` ("All keys matched successfully"), runs deterministically, and produces sane predictions (C_D > 0, no NaNs). ## The core problem and my diagnosis The scored cases live at **higher inlet velocities than any training data** (train 31–75 m/s; public_test 75–82 m/s). I confirmed via the data that the force coefficients are, as physics demands, nearly velocity-invariant: C_L is set by angle of attack (slope ≈ 2π/rad) and shape with negligible velocity dependence, while C_D depends on shape/AoA plus a **weak negative Reynolds (velocity) trend**. The danger is that a naive network overfits the velocity channel , and when I probed one, its predicted C_L blew up unphysically above the training range (0.73→1.24 as velocity went 55→95). ## Key methodological step: an extrapolation-validation harness Because the given val split is in-range, it can't measure extrapolation. I built proxies that hold out the **top velocity band** (train on low velocities, validate on high). One proxy matched the real task's extrapolation distance almost exactly (test 0–8.6% beyond train max), which let me choose strategies on evidence rather than guesswork. ## What I chose A **"shift-up" velocity augmentation** (half of each batch pushed upward into and beyond the test band, targets corrected by the physically-estimated per-target slope , mildly negative for C_D, flat for C_L; the other half kept real to preserve the in-range structure that drives drag), plus **SWA** (tail weight-averaging with BatchNorm stats recomputed) for a single robust checkpoint. This beat the plain baseline on **both** axes: - In-range VAL: **mean_nrmse 0.0875** (cd 0.134, cl 0.041) vs plain 0.0892 - Far-extrapolation proxy: **0.169** vs plain 0.451 and, crucially, its velocity sweep is physically correct , C_L stays flat and C_D gently decreases with velocity across 75→90 m/s, rather than diverging. I verified the choice was robust across seeds (my saved seed was best) and that C_D loss up-weighting didn't help (balanced loss is already near-optimal). A couple of honest caveats: the hidden set's exact velocity ceiling is unknown, so I deliberately favored a robust solution that also holds if it reaches beyond public_test's 82 m/s; and the C_D slope used for augmentation is an AoA-controlled estimate, though its effect over the extrapolation range is small (~8% of a C_D std). `final_train.py` reproduces the checkpoint and `validate_ckpt.py` re-checks it.

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: 9  status: completed  raw_score: 0.08
# reward rule: continuous: score 0.08 < 0.5
# => reward = 0.0  (FAIL)
#
# 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).
----------------------------------------------------------------------
=== final /app contents ===
total 45
drwxrws---  5 h2tagent h2tagent 4096 Jun 19 22:59 .
drwxr-xr-x 26 root     root     4096 Jun 19 21:26 ..
drwxr-sr-x  2 h2tagent h2tagent 4096 Jun 19 22:42 __pycache__
-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-r--r--  1 h2tagent h2tagent 5170 Jun 19 22:42 final_train.py
-rw-r--r--  1 h2tagent h2tagent 7524 Jun 19 22:20 harness.py
drwxr-sr-x  2 h2tagent h2tagent 4096 Jun 19 22:44 outputs
-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
-rw-r--r--  1 h2tagent h2tagent 2049 Jun 19 22:44 validate_ckpt.py
=== deliverable ===
total 347
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 19 22:44 .
drwxrws--- 5 h2tagent h2tagent   4096 Jun 19 22:59 ..
-rw-r--r-- 1 h2tagent h2tagent 346611 Jun 19 22:44 model.pt

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_9f12a436f08c4dfb. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_9f12a436f08c4dfb · verifier authoritative; classifier explanatory.