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.

SubtypeIncomplete Understanding or Hard Extrapolation Problem

EvidenceAgent trajectory shows sophisticated Reynolds analysis (discovering C_D ~ V^-0.21 from training data), principled Reynolds augmentation strategy, and validation on extrapolation-holdout proxy (vel<65→vel≥65) achieving mean_nrmse≈0.11-0.15. However, actual test result is raw_score 0.38 (threshold 0.5 to pass), implying actual test mean_nrmse significantly worse than proxy estimates. Test output confirms 'raw_score: 0.38 < 0.5 => reward = 0.0 (FAIL)'. Agent's physical reasoning was sound but did not fully capture the true test extrapolation difficulty.

Root causeThe agent attempted a sophisticated physics-based extrapolation strategy (Reynolds scaling) validated on a proxy holdout test (vel 65-75), but the actual hidden test regime appears more challenging than the proxy, or the Reynolds augmentation approach's assumptions don't hold as strongly in practice. The gap between proxy validation (~0.11-0.15 NRMSE) and actual test score (~0.38 NRMSE equivalent) indicates the extrapolation problem is harder than captured by the agent's validation harness.

RecommendationN/A - task is fine. This is a legitimately difficult benchmark task requiring sophisticated physics intuition and extrapolation. The instruction clearly specifies the velocity extrapolation challenge. The agent attempted reasonable approaches but the problem exceeds what their method could achieve. For task maintainers: this is appropriate difficulty , baseline oracle passes, agent fails even with physics-informed augmentation, indicating genuine challenge value for a benchmark."

Trajectory

Tool-by-tool agent trajectory

125 tool calls · 3 tool types · 125 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 "---TASK CONFIG---" && cat task_config.json && echo "---FEATURE 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
---TASK 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"]
  }
}
---FEATURE 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 provided code files.

bash

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

output

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

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

from __future__ import annotations

from typing import Mapping

import torch
import torch.nn as nn


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

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


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

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


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

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

from __future__ import annotations

from pathlib import Path

import torch


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


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


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


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

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

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

Now let me explore the actual data to understand the distributions, especially the extrapolation challenge.

bash

cd /app && python -c "
import torch
for split in ['train','val','public_test']:
    p = torch.load(f'data/{split}/split.pt', map_location='cpu', weights_only=True)
    print('===', split, '===')
    print('keys:', list(p.keys()))
    print('points shape/dtype:', p['points'].shape, p['points'].dtype)
    if 'targets' in p:
        print('targets shape/dtype:', p['targets'].shape, p['targets'].dtype)
"

output

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

bash

cd /app && python -c "
import torch
def stats(name, t):
    print(f'{name}: min={t.min().item():.5f} max={t.max().item():.5f} mean={t.mean().item():.5f} std={t.std().item():.5f}')

for split in ['train','val','public_test']:
    p = torch.load(f'data/{split}/split.pt', map_location='cpu', weights_only=True)
    pts = p['points']
    print('======', split, 'N=', pts.shape[0], '======')
    # channels: x, y, inlet_velocity, aoa
    for i,nm in enumerate(['x','y','vel','aoa']):
        stats(f'  ch{i}-{nm}', pts[:,:,i])
    # per-case velocity and aoa are constant across points; take point 0
    vel = pts[:,0,2]; aoa = pts[:,0,3]
    stats('  case-vel', vel)
    stats('  case-aoa', aoa)
    if 'targets' in p:
        t = p['targets']
        stats('  C_D', t[:,0])
        stats('  C_L', t[:,1])
"

output

====== train N= 630 ======
  ch0-x: min=-0.00702 max=1.00000 mean=0.38353 std=0.39556
  ch1-y: min=-0.09970 max=0.16270 mean=0.00791 std=0.03346
  ch2-vel: min=31.28300 max=74.92400 mean=52.97366 std=12.78588
  ch3-aoa: min=-0.08622 max=0.25651 mean=0.07639 std=0.09557
  case-vel: min=31.28300 max=74.92400 mean=52.97366 std=12.79600
  case-aoa: min=-0.08622 max=0.25651 mean=0.07639 std=0.09565
  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 N= 80 ======
  ch0-x: min=-0.00328 max=1.00000 mean=0.38458 std=0.39590
  ch1-y: min=-0.09394 max=0.13663 mean=0.00777 std=0.03233
  ch2-vel: min=31.46800 max=74.16200 mean=54.97725 std=11.39685
  ch3-aoa: min=-0.08360 max=0.25121 mean=0.07986 std=0.09472
  case-vel: min=31.46800 max=74.16200 mean=54.97725 std=11.46848
  case-aoa: min=-0.08360 max=0.25121 mean=0.07986 std=0.09532
  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 N= 100 ======
  ch0-x: min=-0.00553 max=1.00000 mean=0.37608 std=0.39461
  ch1-y: min=-0.09606 max=0.15281 mean=0.00879 std=0.03464
  ch2-vel: min=75.03000 max=81.59300 mean=78.20860 std=1.96583
  ch3-aoa: min=-0.08570 max=0.25820 mean=0.07571 std=0.09605
  case-vel: min=75.03000 max=81.59300 mean=78.20860 std=1.97570
  case-aoa: min=-0.08570 max=0.25820 mean=0.07571 std=0.09653

This is the key insight. The training velocity ranges from 31.3 to 74.9 m/s. The public_test velocity ranges from 75.0 to 81.6 m/s , entirely above the training range. This confirms the extrapolation challenge: the scored (hidden) cases are from the higher-speed, higher-Reynolds end. The public_test velocities (75-81.6) are just above training max (74.9). The hidden test is described as "higher-speed, higher-Reynolds end" , so probably similar to public_test, velocities around 75-82 or maybe even higher. Now, the crucial physics insight: For airfoils, the force coefficients C_D and C_L are *defined* to be dimensionless and normalized by dynamic pressure (0.5 * rho * V^2). In ideal incompressible flow, C_D and C_L are functions of angle of attack and Reynolds number (and shape), NOT of velocity directly. The Reynolds number does depend on velocity, but the dependence of C_L and C_D on Reynolds number is relatively weak compared to the dependence on angle of attack and shape. Wait, but let me check. The AirfRANS dataset. Let me recall. AirfRANS is a dataset of RANS simulations around airfoils. The force coefficients. Actually the key issue: the model takes velocity as an input feature. If velocity is standardized using training mean/std, then at test time velocity values (75-82) will be far outside (several std above training mean of 53, std 12.8 -> (75-53)/12.8 = 1.7, (82-53)/12.8=2.27). So moderately outside. That's extrapolation in the input feature. But the key physics: C_L and C_D in these normalized coefficients should depend weakly on velocity. Let me verify by checking the correlation between velocity and the targets in the training set. Let me think about what determines C_L and C_D: - C_L primarily depends on angle of attack (linear relationship for small angles) and shape. - C_D depends on shape, angle of attack, and Reynolds number (which depends on velocity). Higher Reynolds -> lower skin friction coefficient typically. Let me explore correlations. Actually, this is really important for the extrapolation. Let me think about how to handle velocity. One strong approach: Since C_L and C_D are normalized coefficients, maybe the dependence on velocity is weak. If the model relies heavily on velocity as a feature and it extrapolates, it could go wrong. But there might still be a Reynolds-number effect , let me check the data. Let me compute correlations between case-level velocity, aoa and targets.

The critical observation: training velocities span **31–75 m/s**, but public_test (and the hidden scored set) are **75–82 m/s** , entirely above the training range. This is the extrapolation challenge. Let me understand the underlying physics by examining correlations.

bash

cd /app && python -c "
import torch
p = torch.load('data/train/split.pt', map_location='cpu', weights_only=True)
pts = p['points']; t = p['targets']
vel = pts[:,0,2]; aoa = pts[:,0,3]
cd = t[:,0]; cl = t[:,1]
import numpy as np
def corr(a,b):
    a=a.numpy(); b=b.numpy()
    return np.corrcoef(a,b)[0,1]
print('corr(vel, C_D) =', corr(vel,cd))
print('corr(vel, C_L) =', corr(vel,cl))
print('corr(aoa, C_D) =', corr(aoa,cd))
print('corr(aoa, C_L) =', corr(aoa,cl))
print('corr(aoa^2, C_D) =', corr(aoa**2,cd))
print()
# Does velocity matter once we know shape+aoa? Check how much C_L varies per unit aoa
# Fit linear model C_L ~ aoa
A = torch.stack([aoa, torch.ones_like(aoa)],1)
sol = torch.linalg.lstsq(A, cl).solution
print('C_L ~ aoa: slope,intercept=', sol.tolist())
pred = A@sol
print('C_L linear-in-aoa residual std:', (cl-pred).std().item(), 'vs C_L std', cl.std().item())
"

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
corr(aoa^2, C_D) = 0.8596726372791974

C_L ~ aoa: slope,intercept= [5.682861328125, 0.22548021376132965]
C_L linear-in-aoa residual std: 0.19121742248535156 vs C_L std 0.5761952996253967

This confirms the physics intuition: - C_L is dominated by angle of attack (corr 0.94), very weak velocity dependence (corr -0.02). - C_D is dominated by aoa^2 (corr 0.86) and aoa (corr 0.74), weak velocity dependence (corr -0.15). The velocity has weak correlation with targets. This means the shape (x,y point cloud) and aoa are the primary drivers. The velocity dependence is weak but nonzero, mostly for C_D (Reynolds effect on drag). So the extrapolation in velocity is less dangerous than feared IF the model doesn't overfit to velocity. But the model has velocity as an input channel, standardized. At test, velocity ~1.7-2.3 std above mean. Key strategy considerations: 1. **Normalization of velocity feature**: The big risk is that the network learns some spurious function of velocity that behaves badly when extrapolated. Since the true velocity dependence is weak, I want the model to not over-rely on velocity, OR to represent velocity in a way that extrapolates gracefully. 2. There's a subtlety: the Reynolds number effect on C_D. Higher velocity -> higher Re -> lower C_D (slightly). The corr(vel, C_D) = -0.15 is consistent with that. At higher velocities (test), C_D might be slightly lower. But this effect is weak and confounded with aoa. Let me think about the physics more carefully. In RANS with a fixed airfoil at chord length 1, Reynolds number Re = V*c/nu. For AirfRANS, the kinematic viscosity nu = 1.56e-5 (air). So Re ranges. The drag coefficient for turbulent flow scales roughly as C_D ~ Re^(-0.2) for skin friction. So going from V=53 to V=78, Re increases by factor ~1.47, C_D_friction decreases by factor 1.47^0.2 ~ 1.08, so ~8% decrease in friction drag. Pressure drag less affected. So maybe ~5% decrease in total C_D. That's within the noise but systematic. The question is whether the model can capture this. Honestly, with weak correlation and extrapolation, the safest approach may be to let the model use velocity but ensure good generalization. Actually, let me reconsider. The most important thing for good NRMSE on the hidden set: The metric is mean_nrmse (normalized RMSE). Let me think about what NRMSE normalization is. Probably RMSE divided by something like the std or range of the target, computed per-target, then averaged over the two targets. secondary: worst_nrmse, cd_nmae (normalized MAE for C_D). Let me think about NRMSE definition. NRMSE = RMSE / (max - min) or RMSE / mean or RMSE / std. Since they compute it on the hidden eval set, and they want both targets to matter (mean over the two), normalizing per-target puts C_D and C_L on comparable scales. This is important: C_D is ~0.01 scale, C_L is ~0.6 scale. Without normalization, MSE would be dominated by C_L. So NRMSE normalizes each. This means I should NOT just minimize raw MSE in physical units (C_L would dominate). By standardizing targets (dividing by std), MSE in standardized space treats both targets ~equally, which aligns better with mean_nrmse. Actually let me think: NRMSE for target j = sqrt(mean((pred-true)^2)) / norm_j. If norm_j = std_j (of the eval targets), then NRMSE_j = RMSE_std_j essentially (RMSE in units of std). Minimizing MSE in standardized target space = minimizing sum of (RMSE_std_j)^2. That's well-aligned with minimizing mean NRMSE (if norm = std). But if norm_j = range (max-min), it's proportional to std roughly. Either way, standardizing targets and weighting both equally is the right call. The baseline already standardizes targets. Good. However, I should consider: maybe weight C_D more since it's harder? The metric is mean over the two plus worst_nrmse as secondary. To do well on worst_nrmse I want both to be good, balanced. Standardized MSE is a good default. I might tune per-target weight. Let me think about which target is harder. C_L is strongly determined by aoa, so easy-ish. C_D is small, determined by aoa^2 and shape and Re. C_D relative variation might be harder. Let me actually train the baseline and eval …[truncated]

This is very informative. The physics is clear: - **C_L** is almost entirely determined by angle of attack (corr 0.94) with negligible velocity dependence. - **C_D** is driven by aoa² (corr 0.86) with a weak negative velocity (Reynolds) effect (corr −0.15). So velocity is a weak predictor, but the model gets it as a standardized input that will be pushed 1.7–2.3σ out of range at test time. Let me check compute resources and the evaluation metric definition.

bash

cd /app && python -c "
import torch, time
print('cuda available:', torch.cuda.is_available())
print('torch version:', torch.__version__)
import os
print('cpus:', os.cpu_count())
" && nvidia-smi 2>/dev/null | head -15 || echo "no nvidia-smi"

output

cuda available: False
torch version: 2.5.1+cu124
cpus: 16

No GPU. CPU only with 16 cores. The model is small (PointNet with 256 points), and dataset is small (630 train). So CPU training is feasible. Let me establish the NRMSE metric definition. It's not given explicitly, but I can infer. The primary metric is mean_nrmse. Let me define my own validation metric that's robust: per-target NRMSE normalized by the std (or range) of the true targets. I'll compute both and monitor. Let me think about the evaluation carefully. The hidden eval set is at higher velocity. The val set here is NOT at higher velocity (val vel range 31-74, same as train). So val is in-distribution, not representative of the extrapolation. The public_test is at high velocity (75-82) but has no targets. So I can't directly measure extrapolation performance. This is the crux: I need to build a model that generalizes to higher velocities, but my only labeled data is in the lower velocity range. I have to simulate/anticipate the extrapolation. Strategy to handle velocity extrapolation: Option A: Make the model invariant to velocity (drop velocity or reduce its influence). Given weak correlation, this might not hurt much and would be safe. But we'd lose the Reynolds effect on C_D. Option B: Encode velocity in a way that extrapolates well. E.g., instead of feeding raw velocity standardized by train stats (which makes test values large), feed a physically meaningful transform. But the architecture is fixed: input is [x, y, vel, aoa] with feat_mean/feat_std standardization. I control feat_mean and feat_std! I can choose how velocity is standardized. But the network is an MLP; extrapolation behavior depends on learned weights. Actually, I can't change the input channels (the eval reads points and applies my feat_mean/feat_std then runs forward). The points are fixed as [x,y,vel,aoa]. I can only choose the 4 mean/std values. So velocity will be (vel - feat_mean[2])/feat_std[2]. I can make feat_std[2] large so that test velocities aren't too far out, but that compresses the training signal too. Hmm. Let me think differently. The real lever: how I train. I want the model to: 1. Rely primarily on shape + aoa (which are in-distribution at test). 2. Handle velocity robustly. Key idea: **Velocity augmentation / domain randomization.** Since the true dependence on velocity is weak, I can augment training by randomly perturbing the velocity feature (and even extending it to higher values) WITHOUT changing the targets much. This teaches the network that velocity has limited effect and makes it robust to higher velocities. But I must be careful about the real Reynolds effect on C_D. Actually, a cleaner idea: I can train with velocity values resampled to cover the test range. But I don't have targets for high velocity. However, if I believe the physics (coefficients nearly independent of velocity, or with a known weak Reynolds trend), I can augment. Let me reconsider the magnitude of the Reynolds/velocity effect. corr(vel, C_D) = -0.15 but that's confounded with aoa. Let me do a proper partial analysis: fit C_D ~ f(aoa, shape) and see residual dependence on velocity. Actually shape is high-dimensional. Let me instead look at cases with similar aoa but different velocity. Hard with limited data. Better: fit a linear model C_D ~ [1, aoa, aoa^2, vel] and check the vel coefficient significance. Similarly for C_L. Let me do a multiple regression. Let me also consider Reynolds. Actually, let me reconsider whether velocity even has independent predictive power given aoa and shape. Let me do regression with geometric features. Actually the shape varies (different airfoils). The point cloud gives shape. A PointNet can extract shape. Let me check: is the velocity effect on C_D real and learnable, or noise? Let me do multiple regression to assess.

No GPU, but 16 CPU cores and a small model/dataset , training is feasible on CPU. Let me do a proper multivariate analysis to quantify how much independent predictive power velocity really has (beyond aoa), which determines how I handle the extrapolation.

bash

cd /app && python -c "
import torch, numpy as np
p = torch.load('data/train/split.pt', map_location='cpu', weights_only=True)
pts = p['points']; t = p['targets']
vel = pts[:,0,2].numpy(); aoa = pts[:,0,3].numpy()
cd = t[:,0].numpy(); cl = t[:,1].numpy()
N=len(vel)
def fit(X, y, names):
    X = np.asarray(X).T
    beta, res, rank, sv = np.linalg.lstsq(X, y, rcond=None)
    pred = X@beta
    r2 = 1 - ((y-pred)**2).sum()/((y-y.mean())**2).sum()
    print('  R2=%.4f'%r2, {n:round(float(b),5) for n,b in zip(names,beta)})
    return pred
one=np.ones(N)
print('C_D ~ aoa, aoa^2:')
fit([one,aoa,aoa**2], cd, ['1','aoa','aoa2'])
print('C_D ~ aoa, aoa^2, vel:')
fit([one,aoa,aoa**2,vel], cd, ['1','aoa','aoa2','vel'])
print('C_D ~ aoa, aoa^2, log(vel):')
fit([one,aoa,aoa**2,np.log(vel)], cd, ['1','aoa','aoa2','logv'])
print('C_L ~ aoa:')
fit([one,aoa], cl, ['1','aoa'])
print('C_L ~ aoa, vel:')
fit([one,aoa,vel], cl, ['1','aoa','vel'])
# Reynolds scaling test: does C_D*vel^0.2 reduce velocity dependence?
print()
print('std C_D residual after aoa,aoa2 fit, correlate with vel:')
pred = np.linalg.lstsq(np.array([one,aoa,aoa**2]).T, cd, rcond=None)[0]
predv = np.array([one,aoa,aoa**2]).T@pred
resid = cd-predv
print('  corr(resid, vel)=%.3f corr(resid,aoa)=%.3f'%(np.corrcoef(resid,vel)[0,1], np.corrcoef(resid,aoa)[0,1]))
"

output

C_D ~ aoa, aoa^2:
  R2=0.7405 {'1': 0.00913, 'aoa': -0.00436, 'aoa2': 0.26481}
C_D ~ aoa, aoa^2, vel:
  R2=0.7616 {'1': 0.01221, 'aoa': -0.00454, 'aoa2': 0.26521, 'vel': -6e-05}
C_D ~ aoa, aoa^2, log(vel):
  R2=0.7616 {'1': 0.02071, 'aoa': -0.00442, 'aoa2': 0.26471, 'logv': -0.00294}
C_L ~ aoa:
  R2=0.8899 {'1': 0.22548, 'aoa': 5.68286}
C_L ~ aoa, vel:
  R2=0.8899 {'1': 0.24421, 'aoa': 5.68215, 'vel': -0.00035}

std C_D residual after aoa,aoa2 fit, correlate with vel:
  corr(resid, vel)=-0.285 corr(resid,aoa)=-0.000

So: - For C_D: adding vel improves R2 from 0.7405 to 0.7616. The residual (after removing aoa dependence) correlates -0.285 with velocity. So there IS a real Reynolds effect on C_D: higher velocity -> lower C_D. The coefficient is -6e-5 per m/s. Over the extrapolation from ~53 to ~78 (25 m/s), that's -0.0015 change in C_D, which is ~12% of the mean C_D (0.0128) and ~0.3 std of C_D (std 0.005). So it's a meaningful systematic shift: at high velocity, C_D will be systematically a bit LOWER. This is crucial for extrapolation! If the model ignores velocity, it will systematically over-predict C_D on the high-velocity test set. If the model uses velocity linearly, it can extrapolate the trend. But a neural net might not extrapolate the linear trend well , it could do anything outside the training range. - For C_L: velocity has essentially no effect (R2 unchanged, coef -0.00035 negligible). So C_L depends only on shape + aoa, both in-distribution. C_L should extrapolate fine. So the strategy for the velocity extrapolation: 1. C_L: velocity nearly irrelevant. Safe. 2. C_D: there's a real, roughly log-linear (Reynolds) decreasing trend with velocity. I need the model to extrapolate this trend correctly to higher velocities. The danger: a neural network with velocity as a standardized input, trained only on 31-75 m/s, when given 75-82 m/s, will extrapolate in an uncontrolled way. It might capture the decreasing trend, or it might saturate (GELU/BatchNorm), or it could overshoot. How to make extrapolation of the C_D-vs-velocity trend robust? Idea: Physically, C_D for turbulent flow scales approximately as Re^(-0.2), i.e., log(C_D) linear in log(V). The relationship resid ~ vel is roughly linear/log-linear over the training range. Option 1: Domain randomization on velocity. Augment training data by extending velocity to the test range while adjusting C_D according to the fitted Reynolds trend. But that bakes in my linear assumption and is risky/hacky. Also adjusting targets based on a fitted model is basically injecting a prior. Option 2: Keep velocity as a feature but ensure the learned function is smooth and roughly linear in velocity so it extrapolates sensibly. Hard to guarantee with an MLP. Option 3: Choose feat_std for velocity carefully. If I standardize velocity with a large std, test values are closer to training in standardized units, but still outside. The extrapolation distance in standardized units is (82 - 75)/std beyond the max. Hmm, let me reconsider. The feat_mean/feat_std are applied to ALL channels. The network sees standardized inputs. During training, velocity standardized spans roughly [(31-53)/12.8, (75-53)/12.8] = [-1.7, 1.7]. Test spans [(75-53)/12.8, (82-53)/12.8] = [1.7, 2.27]. So test velocity is in standardized range [1.7, 2.27], just beyond the training max of 1.7. The model must extrapolate the C_D trend from [−1.7,1.7] to [1.7,2.27]. It's a modest extrapolation (0.57 std beyond). If the learned function is smooth and monotonic near the boundary, it'll likely continue the trend reasonably. Given the complexity and risk, I think the best robust approach is: 1. Train a strong in-distribution model with good regularization. 2. Use augmentation that helps generalization: point jitter, point subsampling/dropout, small geometric perturbations. And importantly, velocity augmentation (jitter velocity) to make the velocity response smooth, plus possibly extend velocity range during training with the physically-motivated C_D adjustment. Let me think about whether to inject the Reynolds prior. Actually, the safest high-value move: since I KNOW the test is at higher velocity and I KNOW (from data) that C_D decreases with velocity with a fittable trend, I can help the model by: A) Making the model's velocity dependence correct and extrapolatable. Let me reconsider the whole thing. There might be an even cleaner approach given the fixed architecture. Since I choose feat_mean and feat_std, and these are …[truncated]

Excellent , this is the key finding. Let me quantify precisely: - **C_D ~ aoa,aoa²**: R²=0.74. Adding velocity raises it to 0.76, and the residual C_D correlates **−0.285 with velocity**: a real Reynolds effect where higher speed → lower drag. Over the 53→78 m/s shift, this is ≈ −0.0015 in C_D (~0.3σ), a systematic bias if ignored. - **C_L**: velocity is irrelevant (R² unchanged). So C_L will extrapolate fine; the entire challenge is getting C_D's Reynolds trend to extrapolate correctly. The smartest way to tune for extrapolation is to **simulate it**: hold out the highest-velocity training cases and train only on lower ones. Let me first confirm aoa/shape aren't confounded with velocity, then build this harness.

bash

cd /app && python -c "
import torch, numpy as np
p = torch.load('data/train/split.pt', map_location='cpu', weights_only=True)
pts=p['points']; t=p['targets']
vel=pts[:,0,2].numpy(); aoa=pts[:,0,3].numpy()
print('corr(vel,aoa)=%.3f'%np.corrcoef(vel,aoa)[0,1])
# log-log reynolds exponent for C_D, controlling for aoa,aoa2
cd=t[:,0].numpy(); one=np.ones(len(vel))
X=np.array([one,aoa,aoa**2,np.log(vel)]).T
beta=np.linalg.lstsq(X,np.log(cd),rcond=None)[0]
print('log(C_D) vel-exponent (partial):',round(float(beta[3]),4))
# velocity distribution quantiles
print('vel quantiles:', np.quantile(vel,[0,.6,.7,.8,.9,1.0]).round(2))
print('count vel>=65:', int((vel>=65).sum()), ' vel<65:', int((vel<65).sum()))
print('aoa mean low vs high vel:', round(aoa[vel<65].mean(),3), round(aoa[vel>=65].mean(),3))
"

output

corr(vel,aoa)=-0.015
log(C_D) vel-exponent (partial): -0.2106
vel quantiles: [31.28 57.88 62.32 66.6  70.35 74.92]
count vel>=65: 148  vel<65: 482
aoa mean low vs high vel: 0.078 0.072

- corr(vel, aoa) = -0.015 → velocity and aoa are independent. No confound. Good. - The log-log velocity exponent for C_D is -0.21, which matches the classic turbulent skin-friction Reynolds scaling C_D ~ Re^(-0.2)! This is a strong, physically-meaningful result. So C_D scales roughly as V^(-0.21). - vel>=65: 148 samples, vel<65: 482. Good split for an extrapolation holdout. So I have a clean physical law: C_D ∝ V^(-0.21) (partial, controlling for aoa). This is beautiful because it gives me a principled augmentation: to simulate a case at velocity v', multiply C_D by (v'/v)^(-0.21), keep C_L unchanged. This lets me generate training data at the high velocities of the test set. Now let me set up the extrapolation holdout experiment. Train on vel<65, test on vel>=65. Compare: - (A) baseline: velocity as raw standardized feature, no aug - (B) ignore velocity (constant feature) - (C) velocity feature + Reynolds augmentation (extend velocity with C_D adjustment) Let me build a flexible trainer module and run these. Let me write exp.py. I'll structure: - build dataset tensors - standardizers - augmentation in the training loop - train function returning model + eval metrics on a provided val set Let me define metrics: for predictions vs true (physical units): - per-target RMSE - per-target NRMSE = RMSE/std(true) [std over eval set] - mean_nrmse - C_D mean bias Let me write it. For the Reynolds augmentation, during training I'll, with some probability, resample velocity v' for each sample from a distribution spanning e.g. [31, 90] and set cd' = cd*(v'/v)^(-0.21), cl'=cl. Then standardize with feat stats. Actually better: I'll apply to all samples each batch with fresh random v' to maximize coverage. But I should also keep some original samples. Let me do: for each sample each epoch, with prob p_aug, replace velocity and adjust cd. The exponent -0.21 , I'll treat as a hyperparameter but it's well-grounded. I could also estimate uncertainty. Let me use -0.21. Wait, but there's subtlety: the -0.21 exponent is the PARTIAL effect controlling for aoa,aoa2 linearly, assuming the velocity effect is separable and multiplicative on total C_D. But part of C_D (pressure/induced drag) may not follow this. However, empirically the log-log partial fit gives -0.21 and it improved R2, so as a global approximation for augmentation it's reasonable. Let me also verify by checking: does applying this scaling actually "flatten" the residual velocity dependence? i.e., define cd_corrected = cd * (vel/ref)^(0.21) and check corr with vel in residual. Let me just trust and validate via the holdout experiment: the proof is whether Reynolds-augmented training reduces high-velocity C_D error. Let me build and run. I need to be mindful of compute time on CPU. Model is small; 630 samples; let me time one epoch. Let me write exp.py with a configurable train_eval function.

Two beautiful results: `corr(vel,aoa)=−0.015` (no confound), and the partial **velocity exponent for C_D is −0.21** , exactly the classic turbulent skin-friction Reynolds scaling C_D ∝ Re^(−0.2). This gives me a physically-principled augmentation: to synthesize a case at a new velocity v′, scale C_D by (v′/v)^(−0.21) and leave C_L unchanged. Let me build a flexible trainer plus an **extrapolation-holdout harness** (train on vel<65, test on vel≥65) to objectively compare strategies before committing.

bash

cd /app && cat > exp.py << 'PYEOF'
"""Flexible trainer + extrapolation-holdout harness for model selection."""
from __future__ import annotations
import json, os, sys, time, math
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F

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

with (APP_DIR/'task_config.json').open() as fh:
    CFG = json.load(fh)

def get_data():
    pts, tg = load_split(APP_DIR/'data'/'train')
    return pts, tg

def metrics(pred, true):
    # pred,true: [N,2] physical units
    err = pred-true
    rmse = err.pow(2).mean(0).sqrt()
    mae = err.abs().mean(0)
    std = true.std(0).clamp_min(1e-8)
    nrmse = rmse/std
    bias = err.mean(0)
    return {
        'rmse_cd': rmse[0].item(), 'rmse_cl': rmse[1].item(),
        'nrmse_cd': nrmse[0].item(), 'nrmse_cl': nrmse[1].item(),
        'mean_nrmse': nrmse.mean().item(), 'worst_nrmse': nrmse.max().item(),
        'mae_cd': mae[0].item(), 'bias_cd': bias[0].item(), 'bias_cl': bias[1].item(),
    }

def train_eval(xtr_pts, ytr, xva_pts, yva, *,
               epochs=120, bs=32, lr=1e-3, wd=1e-4, seed=0,
               loss='huber', huber_beta=1.0, w_cd=1.0, w_cl=1.0,
               feat_mean=None, feat_std=None,
               re_aug=0.0, re_exp=-0.21, vel_lo=31.0, vel_hi=90.0,
               pt_jitter=0.0, vel_jitter=0.0, drop_vel=False,
               sched='cosine', verbose=False, seeds_ens=None,
               return_model=False):
    device='cpu'
    pts, tg = xtr_pts, ytr
    N = pts.shape[0]
    # standardizers from training pts/targets
    if feat_mean is None:
        flat = pts.reshape(-1,4); feat_mean = flat.mean(0); feat_std = flat.std(0).clamp_min(1e-8)
    targ_mean = tg.mean(0); targ_std = tg.std(0).clamp_min(1e-8)
    if drop_vel:
        feat_mean = feat_mean.clone(); feat_std = feat_std.clone()
    def standardize(p):
        return (p-feat_mean.view(1,1,-1))/feat_std.view(1,1,-1)

    def run_seed(seed):
        torch.manual_seed(seed); np.random.seed(seed)
        model = build_model(CFG).to(device)
        opt = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=wd)
        if sched=='cosine':
            scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=epochs)
        else:
            scheduler=None
        for ep in range(epochs):
            model.train()
            idx = torch.randperm(N)
            for s in range(0, N, bs):
                bi = idx[s:s+bs]
                pb = pts[bi].clone(); yb = tg[bi].clone()
                # Reynolds velocity augmentation
                if re_aug>0:
                    m = torch.rand(pb.shape[0])<re_aug
                    if m.any():
                        v_old = pb[m,0,2].clone()
                        v_new = torch.empty(m.sum()).uniform_(vel_lo, vel_hi)
                        ratio = (v_new/v_old)
                        pb[m,:,2] = v_new.view(-1,1)
                        yb[m,0] = yb[m,0]*ratio.pow(re_exp)  # cd scaling
                if vel_jitter>0:
                    f = torch.empty(pb.shape[0]).uniform_(1-vel_jitter,1+vel_jitter)
                    pb[:,:,2]=pb[:,:,2]*f.view(-1,1)
                if pt_jitter>0:
                    pb[:,:,:2]=pb[:,:,:2]+torch.randn_like(pb[:,:,:2])*pt_jitter
                xb = standardize(pb)
                yb_s = (yb-targ_mean)/targ_std
                opt.zero_grad(set_to_none=True)
                out = model(xb)
                if loss=='mse':
                    per = (out-yb_s).pow(2)
                else:
                    per = F.huber_loss(out, yb_s, reduction='none', delta=huber_beta)
                wl = per[:,0]*w_cd + per[:,1]*w_cl
                l = wl.mean()
                l.backward(); opt.step()
            if scheduler: scheduler.step()
        model.eval()
        return model
    seeds = seeds_ens if seeds_ens is not None else [seed]
    models = [run_seed(s) for s in seeds]
 
…[truncated]

output

train(low vel) n=482, holdout(high vel) n=148
holdout vel range: 65.0-74.9
baseline(60ep) time=39.4s
  rmse_cd=0.0009
  rmse_cl=0.0680
  nrmse_cd=0.2102
  nrmse_cl=0.1121
  mean_nrmse=0.1612
  worst_nrmse=0.2102
  mae_cd=0.0006
  bias_cd=0.0005
  bias_cl=0.0011

The baseline extrapolation-holdout (train vel<65, test vel>=65): - nrmse_cd=0.21, nrmse_cl=0.11, mean_nrmse=0.16 - bias_cd=+0.0005 (positive: over-predicting C_D on high-velocity holdout, as expected if velocity trend not fully extrapolated). bias is positive meaning pred>true, consistent with the Reynolds effect being under-extrapolated. Now let me compare strategies. The holdout is vel>=65 (65-74.9), while training is vel<65. The gap to the real test (75-82) is larger, but this holdout is the best proxy I have. Note the real test extrapolates further, so biases will be larger there; a method that reduces bias here should help more there. Let me run a comparison of several configs. I'll run each with a couple of seeds for stability, but to save time, single seed first for a sweep, then confirm best with multiple seeds. Configs to compare on the holdout: 1. baseline (re_aug=0) , done: mean_nrmse=0.161, nrmse_cd=0.210, bias_cd=0.0005 2. drop velocity (constant) , expect worse cd bias 3. re_aug=0.5, re_exp=-0.21 4. re_aug=1.0 (always augment), re_exp=-0.21 5. vel_jitter only Let me also vary re_exp to see sensitivity. Let me make the experiment configurable from CLI to run quickly. Actually, let me just write a sweep script that runs several configs and prints a table. To manage time (~40s per 60-epoch run), I'll keep epochs=60 and single seed=0, then refine. Actually, let me reduce variance by averaging 2 seeds for the comparison, but that doubles time. Let me first do single-seed sweep to see big effects, because the Reynolds augmentation effect should be large if real. Let me write a sweep.

The harness works. The baseline extrapolation-holdout (train vel<65 → test vel≥65) gives mean_nrmse=0.161 with **nrmse_cd=0.21 and a positive C_D bias (+0.0005)** , exactly the "over-predict drag at high speed" symptom I predicted. Now let me sweep strategies against this proxy.

bash

cd /app && cat > sweep.py << 'PYEOF'
import time, numpy as np, torch
from exp import get_data, train_eval
pts,tg = get_data()
vel = pts[:,0,2]; tr=vel<65; va=vel>=65
Xtr,Ytr,Xva,Yva = pts[tr],tg[tr],pts[va],tg[va]

def run(name, **kw):
    t0=time.time()
    # average 2 seeds for stability
    ms=[train_eval(Xtr,Ytr,Xva,Yva, seed=s, **kw) for s in (0,1)]
    agg={k:np.mean([m[k] for m in ms]) for k in ms[0]}
    print('%-28s mean_nrmse=%.4f cd=%.4f cl=%.4f bias_cd=%+.5f t=%.0fs'%(
        name, agg['mean_nrmse'], agg['nrmse_cd'], agg['nrmse_cl'], agg['bias_cd'], time.time()-t0))
    return agg

base=dict(epochs=60, loss='huber', huber_beta=1.0)
run('baseline', **base)
run('drop_velocity', drop_vel=True, **base)
run('vel_jitter0.1', vel_jitter=0.1, **base)
run('re_aug0.5_exp-0.21', re_aug=0.5, re_exp=-0.21, **base)
run('re_aug1.0_exp-0.21', re_aug=1.0, re_exp=-0.21, **base)
run('re_aug0.7_exp-0.21', re_aug=0.7, re_exp=-0.21, **base)
run('re_aug0.7_exp-0.30', re_aug=0.7, re_exp=-0.30, **base)
run('re_aug0.7_exp0.0', re_aug=0.7, re_exp=0.0, **base)
PYEOF
python sweep.py

output

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

The sweep timed out because each config runs 2 seeds × 60 epochs ~ 80s, and 8 configs = 640s. I need to run in the background and/or reduce. Let me restart and run experiments in the background, writing results to a file.

The sweep exceeded the 300s limit. Let me restart and run experiments in the background, logging to a file so I can poll results without blocking.

bash

restart: true

output

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

bash

cd /app && cat > sweep.py << 'PYEOF'
import time, numpy as np, torch, sys
from exp import get_data, train_eval
pts,tg = get_data()
vel = pts[:,0,2]; tr=vel<65; va=vel>=65
Xtr,Ytr,Xva,Yva = pts[tr],tg[tr],pts[va],tg[va]
torch.set_num_threads(16)
def run(name, **kw):
    t0=time.time()
    ms=[train_eval(Xtr,Ytr,Xva,Yva, seed=s, **kw) for s in (0,1)]
    agg={k:np.mean([m[k] for m in ms]) for k in ms[0]}
    print('%-28s mean_nrmse=%.4f cd=%.4f cl=%.4f bias_cd=%+.5f t=%.0fs'%(
        name, agg['mean_nrmse'], agg['nrmse_cd'], agg['nrmse_cl'], agg['bias_cd'], time.time()-t0), flush=True)
    return agg
base=dict(epochs=60, loss='huber', huber_beta=1.0)
run('baseline', **base)
run('drop_velocity', drop_vel=True, **base)
run('re_aug0.5_exp-0.21', re_aug=0.5, re_exp=-0.21, **base)
run('re_aug1.0_exp-0.21', re_aug=1.0, re_exp=-0.21, **base)
run('re_aug0.7_exp-0.21', re_aug=0.7, re_exp=-0.21, **base)
run('re_aug0.7_exp-0.30', re_aug=0.7, re_exp=-0.30, **base)
run('re_aug0.7_exp0.0', re_aug=0.7, re_exp=0.0, **base)
print('DONE', flush=True)
PYEOF
nohup python sweep.py > sweep.log 2>&1 &
echo "started pid $!"

output

started pid 349

Let me wait and poll. Each config ~80s, 7 configs = ~560s. Let me check torch threads , I set 16 threads. Let me poll periodically. Meanwhile let me think. Actually, let me check how long a single 60-epoch run takes with 16 threads now; earlier one 60-epoch run was 39s with default threads. 2 seeds = ~80s. 7 configs ~ 560s. I'll poll. While waiting, let me reconsider the design. Let me reconsider whether the holdout proxy is good. The holdout is vel 65-75; training vel<65. But note that the test is vel 75-82, which is beyond even the holdout. Also, the holdout training set (vel<65, n=482) is smaller than the full train (630). For the final model I'll use all 630. Let me also reconsider the exponent. The real test goes to 82. If I augment velocity up to 90, the model sees that range during training, so it won't be extrapolating at all for the high-velocity test , it becomes interpolation! That's the power of the Reynolds augmentation: by generating synthetic high-velocity samples (with physically-adjusted C_D), the model learns the correct velocity response across the whole range, including test. The only assumption is that the exponent -0.21 is correct and that C_L is velocity-independent (both well-supported by data). This is likely to strongly reduce the C_D bias at high velocity. Let me wait for results. Let me poll the log after some time.

While the sweep runs, let me reflect on the design. The Reynolds augmentation is powerful because by generating synthetic samples across velocities up to 90 m/s (with physically-scaled C_D), the model *interpolates* rather than extrapolates at test time , assuming the −0.21 exponent and C_L velocity-independence hold, both well-supported. Let me poll results.

bash

cd /app && sleep 200 && cat sweep.log

output

baseline                     mean_nrmse=0.1628 cd=0.2060 cl=0.1195 bias_cd=+0.00051 t=59s
drop_velocity                mean_nrmse=0.1628 cd=0.2060 cl=0.1195 bias_cd=+0.00051 t=51s
re_aug0.5_exp-0.21           mean_nrmse=0.1395 cd=0.1637 cl=0.1153 bias_cd=+0.00003 t=53s

Interesting. baseline and drop_velocity give identical results , that's suspicious. It suggests that in my train_eval, drop_vel isn't actually doing anything! Let me check: I had `if drop_vel: feat_mean=feat_mean.clone()...` but I never actually zero out the velocity. So drop_vel does nothing. That's a bug, but it doesn't matter much , it just means "drop_velocity" wasn't tested. The identical numbers confirm drop_vel is a no-op. Fine, I don't need it. More importantly: re_aug0.5_exp-0.21 reduces mean_nrmse from 0.163 to 0.140, and crucially nrmse_cd from 0.206 to 0.164, and bias_cd from +0.00051 to +0.00003 (essentially eliminated the bias!). This confirms the Reynolds augmentation works as intended. Let me wait for the rest.

The Reynolds augmentation is clearly working: `re_aug0.5` drops mean_nrmse 0.163→0.140, nrmse_cd 0.206→0.164, and **eliminates the C_D bias (+0.00051→+0.00003)**. (The identical baseline/drop_velocity rows reveal `drop_vel` is a no-op in my code , harmless, I won't use it.) Let me see the rest.

bash

cd /app && sleep 220 && cat sweep.log

output

baseline                     mean_nrmse=0.1628 cd=0.2060 cl=0.1195 bias_cd=+0.00051 t=59s
drop_velocity                mean_nrmse=0.1628 cd=0.2060 cl=0.1195 bias_cd=+0.00051 t=51s
re_aug0.5_exp-0.21           mean_nrmse=0.1395 cd=0.1637 cl=0.1153 bias_cd=+0.00003 t=53s
re_aug1.0_exp-0.21           mean_nrmse=0.1393 cd=0.2225 cl=0.0561 bias_cd=+0.00038 t=49s
re_aug0.7_exp-0.21           mean_nrmse=0.1087 cd=0.1352 cl=0.0822 bias_cd=+0.00004 t=49s
re_aug0.7_exp-0.30           mean_nrmse=0.1158 cd=0.1489 cl=0.0828 bias_cd=-0.00032 t=50s
re_aug0.7_exp0.0             mean_nrmse=0.1918 cd=0.3051 cl=0.0786 bias_cd=+0.00096 t=56s
DONE

Results: - baseline: mean_nrmse=0.163, cd=0.206, cl=0.120, bias_cd=+0.00051 - re_aug0.5_exp-0.21: 0.140, cd=0.164, cl=0.115, bias=+0.00003 - re_aug1.0_exp-0.21: 0.139, cd=0.2225 (!!), cl=0.056, bias=+0.00038 , interesting: always augmenting hurts cd but helps cl a lot. Wait cd went UP to 0.2225 but cl dropped to 0.056. Odd. When re_aug=1.0, every sample's velocity is randomized, so the model never sees original velocity-cd pairing... but cd got worse. And cl improved because maybe the velocity randomization acts as regularization on the shape/aoa features? Actually cl=0.056 is much better. Hmm. - re_aug0.7_exp-0.21: BEST so far: mean_nrmse=0.109, cd=0.135, cl=0.082, bias=+0.00004. - re_aug0.7_exp-0.30: 0.116, cd=0.149, cl=0.083, bias=-0.00032 (over-correct, negative bias). So exp-0.21 is better than -0.30, consistent with the fitted -0.21. - re_aug0.7_exp0.0: 0.192, cd=0.305 (much worse!), bias=+0.00096. This confirms: augmenting velocity with NO cd adjustment (exp 0.0) is BAD , it teaches the model velocity doesn't matter, causing large cd bias and error. This validates that the physical scaling is essential. So the clear winner: re_aug=0.7, exp=-0.21. mean_nrmse=0.109 vs baseline 0.163. That's a 33% improvement on the extrapolation proxy, and nrmse_cd cut from 0.206 to 0.135. The exp=-0.21 (physically fitted) beats both 0.0 and -0.30, confirming the data-driven exponent is right. Now let me refine around re_aug=0.7, exp=-0.21. Let me also tune: - re_aug fraction: 0.6, 0.7, 0.8 - vel_hi: I used 90. Maybe test goes up to 82, so 90 covers it. Could try 85-95. - vel_lo: 31 (train min). Fine. - epochs: more epochs might help. Let me try 100. - loss: huber vs mse; huber_beta. - Add point jitter for regularization. - per-target weights. Also I should consider ensembling (multiple seeds) for the final model, which generally reduces variance/error. Let me also reconsider: the cl nrmse in the holdout is 0.082-0.12. The re_aug helps cl too (from 0.12 to 0.082). Why would velocity augmentation help C_L? Possibly because it regularizes/augments the dataset (more effective data), reducing overfitting. Good side effect. Let me now run a second sweep around the winner, in the background, with more epochs and additional knobs. I'll also add point jitter and test mse vs huber. Let me be efficient and run ~6 configs with 2 seeds each at epochs=80. ~6*2*70s = 840s, too long for one background wait but I can poll in chunks. Actually let me reduce: run key configs. Let me prioritize: 1. re_aug0.7 exp-0.21 epochs=100 (more training) 2. re_aug0.8 exp-0.21 epochs=80 3. re_aug0.6 exp-0.21 epochs=80 4. re_aug0.7 exp-0.21 + pt_jitter0.005 epochs=80 5. re_aug0.7 exp-0.21 mse epochs=80 6. re_aug0.7 exp-0.21 vel_hi=85 epochs=80 7. re_aug0.7 exp-0.21 w_cd=2.0 (weight cd more) epochs=80 Let me also add a held-out based model-selection (early stopping) later. For now compare final-epoch metrics. Let me run these in background. Each ~2 seeds. Let me keep epochs=80 for most to bound time, and run in background, polling. One concern: variance between seeds. 2 seeds may be noisy. But relative trends should hold. I'll confirm final choice with more seeds. Let me write sweep2.py.

Decisive result: **re_aug=0.7, exp=−0.21 is the clear winner: mean_nrmse=0.109 vs baseline 0.163** (a 33% reduction), with nrmse_cd cut from 0.206→0.135 and bias eliminated. Critically, exp=0.0 (velocity aug *without* the physical C_D scaling) is much *worse* (cd=0.305), confirming the −0.21 scaling is essential, and exp=−0.30 over-corrects , so the data-fitted −0.21 is right. Let me refine around this winner.

bash

cd /app && cat > sweep2.py << 'PYEOF'
import time, numpy as np, torch
from exp import get_data, train_eval
pts,tg = get_data()
vel = pts[:,0,2]; tr=vel<65; va=vel>=65
Xtr,Ytr,Xva,Yva = pts[tr],tg[tr],pts[va],tg[va]
torch.set_num_threads(16)
def run(name, seeds=(0,1), **kw):
    t0=time.time()
    ms=[train_eval(Xtr,Ytr,Xva,Yva, seed=s, **kw) for s in seeds]
    agg={k:np.mean([m[k] for m in ms]) for k in ms[0]}
    print('%-30s mean_nrmse=%.4f cd=%.4f cl=%.4f bias_cd=%+.5f t=%.0fs'%(
        name, agg['mean_nrmse'], agg['nrmse_cd'], agg['nrmse_cl'], agg['bias_cd'], time.time()-t0), flush=True)
W=dict(re_aug=0.7, re_exp=-0.21, loss='huber', huber_beta=1.0)
run('w0.7_e100', epochs=100, **W)
run('w0.8_e80', epochs=80, **{**W,'re_aug':0.8})
run('w0.6_e80', epochs=80, **{**W,'re_aug':0.6})
run('w0.7_e80_jit005', epochs=80, pt_jitter=0.005, **W)
run('w0.7_e80_mse', epochs=80, **{**W,'loss':'mse'})
run('w0.7_e80_velhi85', epochs=80, vel_hi=85.0, **W)
run('w0.7_e80_wcd1.5', epochs=80, w_cd=1.5, **W)
print('DONE', flush=True)
PYEOF
nohup python sweep2.py > sweep2.log 2>&1 &
echo "started $!"

output

started 418

Let me wait and poll. ~7 configs * 2 seeds * ~70-90s = ~980-1260s. I'll poll in chunks of ~280s. While waiting, let me think about final model selection strategy and the actual final training. For the final model, I'll train on ALL 630 training samples (not the low-velocity subset) with the winning config, and ensemble a few seeds. I'll use the val split (80 samples, in-distribution velocity 31-74) as a sanity check, but remember it doesn't test extrapolation. I can also create a combined extrapolation check. Also, I should set feat_mean/feat_std. With Reynolds augmentation extending velocity to 90, should I compute feat_mean/feat_std from the original data or from the augmented range? The standardization is applied at inference to test points (vel 75-82). If I compute feat_std from original training data (vel std ~12.8, mean 53), then test vel ~78 standardizes to ~2.0. During training with augmentation, velocities up to 90 are seen, standardizing to ~(90-53)/12.8=2.9. So the model sees standardized velocities up to ~2.9 in training, and test is ~2.0 , within range. Good, consistent. I'll compute feat stats from the original training data (as the baseline does) , this keeps the mapping fixed and sensible. Actually, it doesn't matter much as long as train and inference use the SAME feat_mean/feat_std, and the augmented range covers test. The network learns with whatever standardization. I'll just use original-data stats. Wait, there's a subtlety: in train_eval, feat_mean/feat_std are computed from the training pts BEFORE augmentation (the original velocities). Augmentation changes velocities at batch time, then standardizes with those fixed stats. So during training the model sees standardized velocities beyond the original range (up to (90-53)/12.8≈2.9). At inference, test velocities 75-82 → standardized 1.7-2.3. Covered. Good. For the final model, I'll compute feat stats from the full training set. Let me note: full train vel mean 52.97, std 12.79 (from earlier). Good. Now, another thought: should vel_hi in augmentation be higher than 90 to be safe in case the hidden test has velocities above 82? The task says "higher-speed, higher-Reynolds end". public_test max is 81.6. Hidden likely similar but could be a bit higher. Using vel_hi=90 gives margin. I could even use 95. But pushing vel_hi too high wastes augmentation capacity on velocities that won't appear and might slightly distort. The sweep includes vel_hi=85 vs 90 to check sensitivity. Let me see. Let me also reconsider vel_lo. I set 31 (~train min). Keeping the low end represented is good so the model stays accurate if any lower-velocity cases appear (they won't in test, but keeping balance is fine). Actually, since test is all high-velocity, I might bias augmentation toward higher velocities. But I also want the model to remain generally correct. Since re_aug=0.7 means 30% of samples keep original velocity (which covers 31-75), and 70% get uniform[31,90], the distribution is reasonably covered. Good. Let me wait for sweep2.

Let me reflect on the final plan while this runs. For the final model I'll train on **all 630** samples (the holdout was only to select the method), ensemble several seeds, and compute feat/targ stats from the full set. The augmentation extends velocity to 90 m/s so the test range (75–82) is covered as interpolation. Let me poll.

bash

cd /app && sleep 280 && cat sweep2.log

output

w0.7_e100                      mean_nrmse=0.1394 cd=0.1703 cl=0.1085 bias_cd=+0.00001 t=109s
w0.8_e80                       mean_nrmse=0.1290 cd=0.1791 cl=0.0789 bias_cd=+0.00015 t=75s
w0.6_e80                       mean_nrmse=0.1449 cd=0.2320 cl=0.0578 bias_cd=+0.00032 t=77s

Hmm, there's notable variance. w0.7_e100 gives 0.139 (worse than the e60 run which gave 0.109 earlier). This is concerning , high variance between runs/seeds. The earlier w0.7_e80... wait the earlier sweep had w0.7 at epochs=60 giving 0.109 with seeds (0,1). Now w0.7_e100 gives 0.139. So more epochs made it worse? Or seed variance? The variance is high because: 1. Small holdout (148 samples). 2. BatchNorm + small data + different epochs. 3. Only 2 seeds averaged. This high variance makes single comparisons unreliable. nrmse_cd especially is bouncing (0.135, 0.170, 0.179, 0.232). I need more seeds to get stable estimates, OR a more stable training setup. The cl nrmse also varies a lot (0.108, 0.079, 0.058). This variance is a problem for reliable model selection. Let me think about reducing it: - More seeds (e.g., 4-5) per config , but expensive. - Ensemble within each config (the final model will ensemble anyway). - Reduce BatchNorm variance: but architecture is fixed. - The holdout proxy has limited samples. Given the variance, the robust conclusion from sweep1 + sweep2 so far: re_aug in [0.7, 0.8] with exp -0.21 helps substantially over baseline (0.13-0.14 vs 0.16), and bias_cd is near zero. The exact optimum among 0.6/0.7/0.8 is within noise. Let me wait for the rest of sweep2 (jitter, mse, velhi85, wcd1.5), then make a decision. But given variance, I should base the final decision on multiple-seed averages and maybe a different, more stable evaluation. Actually, a better idea to reduce evaluation variance: use k-fold-like multiple holdouts or evaluate the ensemble. But time is limited. Let me reconsider: the REAL goal is the hidden test at vel 75-82. My holdout is vel 65-75. There's inherent proxy mismatch. The key robust findings: 1. Reynolds augmentation with exp≈-0.21 removes the C_D high-velocity bias and improves nrmse_cd. This is physically grounded and confirmed across multiple runs (bias_cd near 0 consistently with aug, vs +0.0005 baseline). This is the main win and I'm confident in it. 2. re_aug fraction 0.7-0.8 good. 3. The exact epochs/loss/jitter are second-order and noisy. For the FINAL model, to combat variance, I'll ensemble MANY seeds (e.g., 8-10). Ensembling reduces the variance dramatically and typically improves accuracy. This is the most reliable lever given the noise. Let me wait for the rest of sweep2, then run a focused, higher-seed comparison of the top 2-3 configs to finalize, then build the ensemble final model. Let me poll again.

I notice substantial run-to-run variance (e.g., w0.7 gave 0.109 earlier but 0.139 now), driven by the small 148-sample holdout and BatchNorm on small data. The *robust* finding across all runs is that Reynolds aug with exp≈−0.21 and fraction 0.7–0.8 consistently removes the C_D bias and helps , the exact sub-config is within noise. This tells me the final model should **ensemble many seeds** to beat the variance. Let me see the remaining configs.

bash

cd /app && sleep 270 && cat sweep2.log

output

w0.7_e100                      mean_nrmse=0.1394 cd=0.1703 cl=0.1085 bias_cd=+0.00001 t=109s
w0.8_e80                       mean_nrmse=0.1290 cd=0.1791 cl=0.0789 bias_cd=+0.00015 t=75s
w0.6_e80                       mean_nrmse=0.1449 cd=0.2320 cl=0.0578 bias_cd=+0.00032 t=77s
w0.7_e80_jit005                mean_nrmse=0.1876 cd=0.2654 cl=0.1099 bias_cd=+0.00028 t=77s
w0.7_e80_mse                   mean_nrmse=0.1888 cd=0.3042 cl=0.0734 bias_cd=+0.00038 t=75s
w0.7_e80_velhi85               mean_nrmse=0.1135 cd=0.1625 cl=0.0646 bias_cd=+0.00011 t=78s

More results: - w0.7_e80_jit005: 0.188 , point jitter HURTS. Skip jitter. - w0.7_e80_mse: 0.189, cd=0.304 , MSE much worse than Huber for cd! Huber is important (robust to C_D outliers). Keep Huber. - w0.7_e80_velhi85: 0.1135, cd=0.1625, cl=0.0646 , good, similar to velhi90. So: Huber >> MSE. No jitter. re_aug 0.7-0.8. velhi 85-90 similar. Let me wait for the last one (wcd1.5) then finalize. Actually there's one more: w0.7_e80_wcd1.5. Let me poll once more briefly. The MSE result is important: MSE gives cd=0.304 vs Huber cd≈0.16-0.18. Why? Because in standardized target space, C_D has outliers (some high-drag cases at high aoa). MSE chases those and the velocity-augmented C_D values create a spread; Huber is robust. Also Huber with delta=1.0 in standardized space means errors beyond 1 std are linear. Good. Let me confirm the final choice: - loss = huber, beta=1.0 - re_aug = 0.7 (or 0.75), exp=-0.21 - vel_hi=90 (safe margin), vel_lo=31 - epochs: 60-80 (e60 gave good results; e100 didn't help). Let me pick ~70. - no jitter - cosine schedule - ensemble many seeds Let me get the last result then do a final multi-seed confirmation run comparing re_aug 0.7 vs 0.8 with 4 seeds each, and also decide epochs. Actually, given variance and time, let me just lock in a robust config and focus on building the ensemble final model + validating. Let me decide epochs more carefully with a quick check, because e60 (sweep1) gave 0.109 but e100 gave 0.139 , could be seed noise or genuine overfitting with more epochs. Let me test epochs with more seeds for stability. I'll run re_aug0.7 at epochs 50, 70 with 4 seeds each. And re_aug0.8 epochs 70 with 4 seeds. That's 12 runs ~ 12*65s=780s. Too long for one call; background it. Actually, let me be more decisive to conserve tokens/time. Key robust settings locked: Huber, re_aug~0.75, exp-0.21, no jitter, cosine. For epochs, moderate (around 60-70) to avoid overfit. The final model ensembles ~10 seeds which will stabilize. Let me do ONE more confirmation: multi-seed (4 seeds) for the near-final config at epochs=70, re_aug=0.75, and also evaluate the ENSEMBLE of those 4 seeds on the holdout (ensemble is what I'll ship). This tells me the realistic shipped performance. Let me wait for wcd then run this.

Clear conclusions: **Huber loss is essential** (MSE gives cd=0.304 vs 0.16), **point jitter hurts**, and velhi 85/90 are equivalent. Let me get the last config, then run a multi-seed confirmation that also measures the **ensemble** performance (what I'll actually ship).

bash

cd /app && sleep 30 && tail -2 sweep2.log

output

w0.7_e80_wcd1.5                mean_nrmse=0.1174 cd=0.1676 cl=0.0672 bias_cd=+0.00013 t=82s
DONE

w0.7_e80_wcd1.5: 0.1174, cd=0.1676, cl=0.0672. Weighting cd 1.5x doesn't clearly help (cl slightly worse, cd similar). The default equal weighting is fine. Given the metric averages both and worst_nrmse matters, balanced is good. Now let me add an ensemble-evaluation capability to exp.py (train_eval already supports seeds_ens for ensemble). Let me run a confirmation comparing: - single-seed avg vs ensemble for re_aug=0.75, exp-0.21, huber, e70 - also test epochs 50 vs 70 Let me use the seeds_ens param of train_eval which builds multiple models and averages predictions. I'll evaluate ensemble of 5 seeds for a couple configs on the holdout. Let me write conf.py: - config A: re_aug0.75 e60 ens5 - config B: re_aug0.75 e80 ens5 - config C: re_aug0.8 e70 ens5 Each ensemble of 5 = 5 trainings ~ 5*65s=325s per config. 3 configs ~975s. Too long. Let me do 2 configs, ens of 5, background, poll. Actually to save time, ensemble of 4 seeds, 2 configs: A=re_aug0.75 e70, B=re_aug0.8 e70. ~4*65*2=520s. Background and poll. Let me also include a single larger ensemble for the winner to estimate final perf. Let me write conf.py.

Weighting C_D doesn't help; equal weighting is best for the balanced metric. My config is locked: **Huber, re_aug≈0.75, exp=−0.21, no jitter, cosine**. Now let me confirm with multi-seed ensembles (what I'll ship), comparing a couple of re_aug/epoch settings.

bash

cd /app && cat > conf.py << 'PYEOF'
import time, numpy as np, torch
from exp import get_data, train_eval
pts,tg = get_data()
vel = pts[:,0,2]; tr=vel<65; va=vel>=65
Xtr,Ytr,Xva,Yva = pts[tr],tg[tr],pts[va],tg[va]
torch.set_num_threads(16)
def run(name, seeds, **kw):
    t0=time.time()
    m=train_eval(Xtr,Ytr,Xva,Yva, seeds_ens=list(seeds), **kw)
    print('%-26s ENS%d mean_nrmse=%.4f cd=%.4f cl=%.4f worst=%.4f bias_cd=%+.5f t=%.0fs'%(
        name, len(seeds), m['mean_nrmse'], m['nrmse_cd'], m['nrmse_cl'], m['worst_nrmse'], m['bias_cd'], time.time()-t0), flush=True)
W=dict(re_exp=-0.21, loss='huber', huber_beta=1.0)
run('re0.75_e70', range(5), epochs=70, re_aug=0.75, **W)
run('re0.80_e70', range(5), epochs=70, re_aug=0.80, **W)
run('re0.75_e60', range(5), epochs=60, re_aug=0.75, **W)
print('DONE', flush=True)
PYEOF
nohup python conf.py > conf.log 2>&1 &
echo "started $!"

output

started 495

This will take ~15 seeds * 65s ≈ 975s ≈ 16 min. Let me poll in chunks. Meanwhile, let me prepare the final training script so I'm ready to launch it once I confirm the config. Let me think about the final model: - Train on ALL 630 samples. - Config: huber, re_aug~0.75, exp-0.21, vel_hi=90, vel_lo=31, cosine, epochs~70. - Ensemble ~8-10 seeds. The checkpoint must contain a single state_dict though! The grader loads ONE model and runs forward. I cannot ship an ensemble of separate models unless I can average their weights... Wait. The checkpoint format is a single state_dict that loads into ONE PointNetRegressor. I cannot ship a prediction-averaging ensemble because the grader runs a single forward pass on the single model. So ensembling by averaging PREDICTIONS is not directly shippable. Options: 1. Weight averaging (model soup): average the state_dicts of multiple independently-trained models. This works ONLY if they're in the same loss basin; otherwise averaging weights gives garbage. For models trained from different random inits, naive weight averaging usually fails. BUT there's a known trick: if they share the same init and data order differences are small... Actually "model soup" works when models are fine-tuned from the SAME pretrained init. Training from scratch with different seeds → different permutations of neurons → weight averaging fails. 2. To get ensemble benefits in a single model, I could train ONE model but with good regularization and longer training. Or use weight averaging over the TRAJECTORY of a single training run (SWA - Stochastic Weight Averaging), which averages weights along the training path , these are in the same basin and average well. SWA is shippable as a single state_dict and often improves generalization! 3. Or I train multiple seeds from the SAME initialization and average weights (a "soup"). Could work if they stay in the same basin, but from scratch on different data orders they may diverge. Risky. So the realistic options for a SINGLE shippable model: - A single well-trained model (best single seed). - SWA: average weights over the last part of training (same run) → single state_dict, in-basin, improves generalization. BatchNorm running stats need recomputation after averaging. - Model soup from shared init: uncertain. My conf.py evaluates prediction-ensembles, which OVERESTIMATES what I can ship (since I can't ship pred-ensemble). I should instead evaluate what I CAN ship: single model, and SWA. Let me reconsider. Actually, can I ship a prediction ensemble? The grader does: load checkpoint dict, model.load_state_dict(ckpt['state_dict']), standardize with feat_mean/std, forward, destandardize. Single model. So NO, I must produce one state_dict. Therefore, SWA is the best bet for ensemble-like robustness in a single model. Let me implement SWA: during the final epochs, periodically snapshot weights and average them. Then recompute BatchNorm statistics by a forward pass over training data in train mode (update_bn). Ship the averaged weights + recomputed BN. Alternatively, a simpler robust approach: just train a single model with the good config and enough epochs; rely on the augmentation for generalization. The seed variance means a single model is a gamble. SWA reduces that. Let me reconsider: the variance I saw was across seeds AND the metric is on a small noisy holdout. For the real test (100+ cases presumably), variance of the metric is smaller. Still, a single model's quality varies by seed. SWA helps. Let me pivot conf.py interpretation: the prediction-ensemble numbers give a lower bound on achievable error and confirm config ranking, but my shipped model will be single/SWA. Let me also evaluate SWA on the holdout. Actually, let me reconsider whether prediction-ensemble is truly unshippable. Hmm, what if I store the ensemble but... no, the state_dict maps to one model. Confirmed single. Let me implement SWA in a training function and test it on the holdout, comparing s …[truncated]

I realize a critical constraint: the grader loads a **single** `state_dict` into one model, so I cannot ship a prediction-averaging ensemble. The right way to get ensemble-like robustness in one checkpoint is **SWA (Stochastic Weight Averaging)** , averaging weights along one training trajectory (same basin, so averaging is valid), then recomputing BatchNorm stats. Let me add SWA support to my trainer while the confirmation run proceeds.

bash

cd /app && cat >> exp.py << 'PYEOF'


def train_swa_eval(xtr_pts, ytr, xva_pts, yva, *,
                   epochs=80, swa_start=45, swa_lr=5e-4, bs=32, lr=1e-3, wd=1e-4, seed=0,
                   loss='huber', huber_beta=1.0, w_cd=1.0, w_cl=1.0,
                   re_aug=0.75, re_exp=-0.21, vel_lo=31.0, vel_hi=90.0,
                   feat_mean=None, feat_std=None, bn_update_passes=8,
                   return_model=False):
    """Single-run SWA: cosine to swa_start, then constant swa_lr collecting weight avg."""
    from torch.optim.swa_utils import AveragedModel, update_bn
    device='cpu'; pts,tg = xtr_pts, ytr; N=pts.shape[0]
    if feat_mean is None:
        flat=pts.reshape(-1,4); feat_mean=flat.mean(0); feat_std=flat.std(0).clamp_min(1e-8)
    targ_mean=tg.mean(0); targ_std=tg.std(0).clamp_min(1e-8)
    def standardize(p): return (p-feat_mean.view(1,1,-1))/feat_std.view(1,1,-1)
    def aug_batch(pb, yb):
        if re_aug>0:
            m=torch.rand(pb.shape[0])<re_aug
            if m.any():
                v_old=pb[m,0,2].clone(); v_new=torch.empty(int(m.sum())).uniform_(vel_lo,vel_hi)
                pb[m,:,2]=v_new.view(-1,1); yb[m,0]=yb[m,0]*(v_new/v_old).pow(re_exp)
        return pb, yb
    torch.manual_seed(seed); np.random.seed(seed)
    model=build_model(CFG).to(device)
    opt=torch.optim.Adam(model.parameters(), lr=lr, weight_decay=wd)
    sched=torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=swa_start)
    swa_model=AveragedModel(model)
    n_avg=0
    for ep in range(epochs):
        model.train(); idx=torch.randperm(N)
        for s in range(0,N,bs):
            bi=idx[s:s+bs]; pb=pts[bi].clone(); yb=tg[bi].clone()
            pb,yb=aug_batch(pb,yb)
            xb=standardize(pb); yb_s=(yb-targ_mean)/targ_std
            opt.zero_grad(set_to_none=True); out=model(xb)
            per=(out-yb_s).pow(2) if loss=='mse' else F.huber_loss(out,yb_s,reduction='none',delta=huber_beta)
            (per[:,0]*w_cd+per[:,1]*w_cl).mean().backward(); opt.step()
        if ep<swa_start:
            sched.step()
        else:
            for g in opt.param_groups: g['lr']=swa_lr
            swa_model.update_parameters(model); n_avg+=1
    # finalize: use swa weights if collected else last model
    final = swa_model.module if n_avg>0 else model
    # recompute BN stats over augmented training distribution
    def bn_loader():
        for _ in range(bn_update_passes):
            idx=torch.randperm(N)
            for s in range(0,N,bs):
                bi=idx[s:s+bs]; pb=pts[bi].clone(); yb=tg[bi].clone()
                pb,_=aug_batch(pb,yb)
                yield standardize(pb)
    # manual BN reset+update
    for m_ in final.modules():
        if isinstance(m_, torch.nn.modules.batchnorm._BatchNorm):
            m_.reset_running_stats(); m_.momentum=None
    final.train()
    with torch.no_grad():
        for xb in bn_loader(): final(xb)
    final.eval()
    with torch.no_grad():
        pred=final(standardize(xva_pts))*targ_std+targ_mean
    m=metrics(pred, yva)
    if return_model:
        return m, final, (feat_mean,feat_std,targ_mean,targ_std)
    return m
PYEOF
echo "added train_swa_eval"; sleep 120; cat conf.log

output

added train_swa_eval

conf.log is empty still (first ensemble of 5 takes ~325s). Let me wait more. Actually, let me reconsider the time. conf.py: 3 configs, each ens of 5 seeds, each seed ~65s → each config ~325s, total ~975s. First result at ~325s. I've waited ~120s after starting + earlier. Let me wait more and then test SWA separately. Let me wait and poll conf.log.

bash

cd /app && sleep 230 && cat conf.log

output

re0.75_e70                 ENS5 mean_nrmse=0.1607 cd=0.2661 cl=0.0552 worst=0.2661 bias_cd=+0.00049 t=221s
re0.80_e70                 ENS5 mean_nrmse=0.1026 cd=0.1542 cl=0.0510 worst=0.1542 bias_cd=+0.00015 t=154s

Interesting and concerning: the 5-seed prediction ensemble: - re0.75_e70 ENS5: mean_nrmse=0.161, cd=0.266 (!), cl=0.055, bias=+0.00049 - re0.80_e70 ENS5: mean_nrmse=0.103, cd=0.154, cl=0.051, bias=+0.00015 Wait, re0.75_e70 ensemble gives cd=0.266 which is WORSE than the 2-seed average earlier (0.135-0.17). That's strange , an ensemble should be better than or comparable to individuals. Unless the individual seeds for re0.75 at e70 happened to have high cd bias. The bias_cd=+0.00049 for re0.75 ensemble is high (like baseline!), while re0.80 ensemble bias=0.00015. This is very noisy. The difference between re0.75 and re0.80 giving such different results (0.161 vs 0.103) suggests the holdout metric is unstable, OR there's something about the specific seeds. Hold on. With ensemble of 5 seeds, the result should be fairly stable (averaging 5 models). Yet re0.75 ens cd=0.266 is much worse than re0.80 ens cd=0.154. The only difference is re_aug 0.75 vs 0.80. A 5% change in augmentation fraction shouldn't cause 0.266 vs 0.154. This must be partly the specific randomness, but with 5-seed ensembling it's surprising. Wait , maybe there's a subtle issue: the ensemble averages predictions in standardized space across 5 models, but each model was trained identically except seed. The variance across the ensemble mean should be low. The fact that re0.75 ens has bias +0.00049 (high) means ALL 5 seeds at re0.75 e70 systematically over-predicted cd. That points to re0.75 e70 being genuinely worse at removing bias than re0.80 e70? That doesn't make physical sense , more aug (0.80) removes bias slightly better, plausible, but the magnitude of difference is suspicious. I think the holdout (148 high-vel samples, vel 65-75) combined with training on vel<65 is just noisy, and these point estimates have high variance even with 5-seed ensembles because the ensembles share the same training DATA (only seed differs), so systematic data-driven quirks persist. Let me step back. The big, reliable picture from ALL experiments: - Reynolds augmentation with exp≈-0.21 dramatically helps vs baseline (bias removal + lower cd error). CONFIRMED repeatedly. - cl nrmse with aug+ensemble is ~0.05, very good. - cd nrmse varies 0.13-0.27 depending on run; the ensemble of re0.80 gives 0.154. - Best observed: re0.80_e70 ENS5 = 0.103 mean. The instability in cd is concerning but note: the real test is different from this holdout. I should not over-tune to this noisy proxy. The robust, physically-motivated choices are what matter: 1. Reynolds velocity augmentation, exp=-0.21, high fraction (0.75-0.8). 2. Huber loss. 3. SWA for a stable single shippable model. Let me now test SWA on the holdout to see if it's competitive and stable. Let me wait for the third conf result (re0.75_e60 ens5) and then run SWA tests. Actually conf.py still has re0.75_e60 running. Let me not wait for it (it's noisy anyway). Let me start SWA experiments in parallel? No, CPU is shared (16 threads, already saturated by conf.py). Let me wait for conf.py to finish, then run SWA tests. Let me wait for conf to finish (~100s more for re0.75_e60). Let me reconsider the variance issue more carefully because it affects my confidence. One possibility: the high variance in cd is because some seeds produce models where the velocity augmentation interacts badly. But more likely it's the small holdout. Actually, let me reconsider the whole evaluation. A cleaner, lower-variance proxy: instead of a single low/high split at 65, I could evaluate on the val set (80 samples) for the in-distribution part AND use the high-vel holdout for extrapolation. But val is in-distribution (vel<75). Alternatively, I could do the extrapolation holdout with MORE high-vel samples by lowering the threshold (e.g., train vel<60, test vel>=60 gives more test samples but fewer train). Or average over multiple thresholds. Given token/time budget, let me be pragmatic: - Lock the physically-motivated config (Reynolds aug exp-0.21, …[truncated]

The 5-seed ensembles are still noisy (re0.75 cd=0.266 vs re0.80 cd=0.154 , too large a gap for a 5% aug change), confirming the 148-sample holdout is a high-variance proxy. I shouldn't over-tune to it. The **robust, physically-grounded** choices (Reynolds aug exp=−0.21 at high fraction, Huber loss) are what matter. Let me wait for the last run, then validate SWA.

bash

cd /app && sleep 130 && cat conf.log

output

re0.75_e70                 ENS5 mean_nrmse=0.1607 cd=0.2661 cl=0.0552 worst=0.2661 bias_cd=+0.00049 t=221s
re0.80_e70                 ENS5 mean_nrmse=0.1026 cd=0.1542 cl=0.0510 worst=0.1542 bias_cd=+0.00015 t=154s
re0.75_e60                 ENS5 mean_nrmse=0.1208 cd=0.1794 cl=0.0621 worst=0.1794 bias_cd=+0.00027 t=132s
DONE

re0.75_e60 ENS5: 0.121, cd=0.179, cl=0.062. So re0.75 e60 is better than re0.75 e70 (0.161). More epochs (70 vs 60) at re0.75 made it worse. And re0.80 e70 was best (0.103). This is confusing/noisy. The cl nrmse is consistently good (~0.05-0.06) with ensembling. The cd is the noisy one (0.15-0.27). Let me make peace with the noise and lock decisions based on robustness: - re_aug: 0.75-0.80. I'll go with 0.8 (best observed and more aug = more high-vel coverage = safer for extrapolation). Actually, more augmentation means more synthetic high-velocity samples, which directly helps the test regime. Given the test is ALL high-velocity, leaning toward more aug (0.8) is sensible. But too much (1.0) removed original pairing and hurt earlier. 0.8 is a good middle. - epochs: the effect is noisy. I'll use SWA which averages over epochs and is less sensitive to exact epoch count. - Huber, exp-0.21, vel_hi=90. Now let me test SWA on the holdout with the chosen config, and compare single-seed vs SWA for stability. Let me run SWA for a few seeds and also check bias. Let me write swa_test.py: - For seeds 0..3: train_swa_eval(re_aug=0.8, exp-0.21, epochs=80, swa_start=45, swa_lr=5e-4) on holdout, print metrics. - Compare to see stability and bias. Time: 4 seeds * (~80 epochs training + BN passes). 80 epochs ~ a bit more than 60 epochs (~50s) → ~65s + BN 8 passes (~10s) = ~75s each. 4 seeds ~ 300s. Background it. Actually let me also compare SWA vs plain to confirm SWA helps stability. Let me just run SWA for 4 seeds and also one plain for reference. Background, poll. Let me also reduce bn_update_passes to save time; 5 passes enough. Let me write and launch.

Last run confirms the noise (re0.75 is better at e60 than e70 , not physically meaningful). C_L is consistently excellent (~0.05); C_D is the noisy dimension. I'll lock in **re_aug=0.8** (more synthetic high-velocity coverage directly helps the all-high-velocity test) and use **SWA** to be robust to the epoch-count sensitivity. Let me validate SWA across seeds on the holdout.

bash

cd /app && cat > swa_test.py << 'PYEOF'
import time, numpy as np, torch
from exp import get_data, train_swa_eval, train_eval
pts,tg = get_data()
vel = pts[:,0,2]; tr=vel<65; va=vel>=65
Xtr,Ytr,Xva,Yva = pts[tr],tg[tr],pts[va],tg[va]
torch.set_num_threads(16)
W=dict(re_aug=0.8, re_exp=-0.21, loss='huber', huber_beta=1.0, vel_hi=90.0)
print('--- SWA per-seed (holdout vel>=65) ---', flush=True)
swres=[]
for s in range(4):
    t0=time.time()
    m=train_swa_eval(Xtr,Ytr,Xva,Yva, seed=s, epochs=80, swa_start=45, swa_lr=5e-4, bn_update_passes=6, **W)
    swres.append(m)
    print('swa seed%d mean=%.4f cd=%.4f cl=%.4f bias_cd=%+.5f t=%.0fs'%(s,m['mean_nrmse'],m['nrmse_cd'],m['nrmse_cl'],m['bias_cd'],time.time()-t0), flush=True)
agg={k:np.mean([m[k] for m in swres]) for k in swres[0]}
sd={k:np.std([m[k] for m in swres]) for k in swres[0]}
print('SWA avg mean=%.4f(+-%.4f) cd=%.4f(+-%.4f) cl=%.4f bias_cd=%+.5f'%(agg['mean_nrmse'],sd['mean_nrmse'],agg['nrmse_cd'],sd['nrmse_cd'],agg['nrmse_cl'],agg['bias_cd']), flush=True)
print('DONE', flush=True)
PYEOF
nohup python swa_test.py > swa_test.log 2>&1 &
echo "started $!"

output

started 573

Let me wait ~300s for the 4 SWA runs. Meanwhile, let me prepare the final training script that will produce /app/outputs/model.pt. The final model: - Train on ALL 630 samples. - Config: re_aug=0.8, exp-0.21, huber, vel_hi=90, vel_lo=31, SWA (epochs=80, swa_start=45, swa_lr=5e-4), bn recompute. - feat_mean/feat_std from full training set, targ_mean/targ_std from full training set. - Save checkpoint with state_dict + 4 norm tensors. But wait , should I consider a "soup" of multiple SWA models? No, single model only. SWA already gives weight averaging within a run. Should I pick the best of several SWA seeds based on... what? I can't use the holdout for final (I'll train on all data). I could train the final on all data with a fixed seed, or train a few seeds and... I can only ship one. Without a reliable selection metric on all-data (val is in-distribution only), I'll just use a fixed seed. But to hedge, I could select among a few final candidates using the val set + a self-constructed high-vel check. Actually, the val set is in-distribution (vel<75), so it tests general fit but not extrapolation. I can ALSO evaluate each final candidate on the extrapolation holdout by... no, the final is trained on all data including high-vel, so holdout is contaminated. Simplest robust approach: train the final model on all data with SWA and a fixed seed. SWA reduces seed sensitivity. Possibly average a couple of SWA runs via "soup" from the same init , but different seeds have different inits here. Alternative for extra robustness: train ONE long SWA run that collects many snapshots (e.g., epochs=120, swa_start=60, collecting 60 snapshots). More snapshots → smoother average → more stable. This is a single run, single model. Good. Let me also reconsider: should the final model use the val set for anything? I could merge train+val for MORE training data (710 samples). The val set is in-distribution; including it adds data. Since the grader uses a separate hidden set, using val for training is fine and gives more data. But I lose my in-distribution sanity check. I can still sanity-check on public_test (ranges) and on val BEFORE merging (train-only model) then retrain on train+val for final. Actually, to maximize data for the final model, merging train+val is beneficial (more shapes/conditions, especially val's higher velocities up to 74 help the high-vel regime). Let me merge train+val for the final model. Wait, val max vel is 74.16, train max 74.92. Merging adds 80 samples across the same ranges. More data → better. I'll merge for the final. Let me decide: Final = train on train+val (710 samples), SWA, re_aug=0.8 exp-0.21 huber, vel_hi=90, epochs ~100, swa_start ~55, swa_lr 5e-4, bn recompute over augmented dist. Ensemble? No, single SWA. Actually, let me reconsider whether I can get ensemble benefit in one model via "SWA across multiple independent runs sharing a common pre-trained init." I could: (1) train a base model for K epochs, (2) branch into M runs with different seeds/data orders for a few epochs each with high LR, collecting snapshots into one average. Since they share the base init and only diverge briefly, weight averaging may remain valid and capture some diversity. This is like SWA with restarts. Possibly marginal. Given time, I'll stick with standard SWA (long run, many snapshots). Let me also double-check: does SWA preserve the low cd bias? The swa_test will tell me. Let me prepare the final script now (I'll finalize parameters after seeing swa_test). Let me write it but not run yet. Let me also make sure about a subtlety in SWA + BatchNorm: AveragedModel copies the model including BN buffers, but averaging BN running stats via update_parameters also averages buffers by default? In recent torch, AveragedModel has use_buffers param (default False), meaning buffers (BN running stats) are NOT averaged , they're taken from the last model. That's why update_bn is needed. I do recompute BN manually, so it's fine. Good. O …[truncated]

While SWA validates, let me prepare the final training script. Key decisions: train on **train+val merged** (710 samples , more data, and val's velocities up to 74 strengthen the high-speed regime), use a long SWA run with many snapshots for a stable single checkpoint, and recompute BatchNorm stats over the augmented distribution. Let me check on the SWA results first.

bash

cd /app && sleep 280 && cat swa_test.log

output

--- SWA per-seed (holdout vel>=65) ---
swa seed0 mean=0.1115 cd=0.1636 cl=0.0595 bias_cd=+0.00013 t=47s
swa seed1 mean=0.1045 cd=0.1475 cl=0.0615 bias_cd=+0.00013 t=40s
swa seed2 mean=0.1125 cd=0.1499 cl=0.0750 bias_cd=+0.00006 t=40s
swa seed3 mean=0.1027 cd=0.1505 cl=0.0549 bias_cd=+0.00009 t=40s
SWA avg mean=0.1078(+-0.0043) cd=0.1529(+-0.0063) cl=0.0627 bias_cd=+0.00011
DONE

SWA is MUCH more stable across seeds: - seed0: 0.1115, seed1: 0.1045, seed2: 0.1125, seed3: 0.1027 - avg 0.1078 ± 0.0043 (std only 0.004!), cd=0.153±0.006, cl=0.063, bias_cd=+0.00011. Compare to single models (sweep had cd bouncing 0.13-0.27). SWA std of 0.004 on mean_nrmse and 0.006 on cd is excellent stability. And the values are good: mean 0.108, matching the best prediction-ensemble (0.103). So SWA gives ensemble-like performance AND stability in a single shippable model! bias_cd is consistently ~+0.0001 (tiny), confirming the Reynolds augmentation + SWA removes the high-velocity drag bias robustly. This is a great outcome. Decision: ship an SWA model. Now let me finalize. Config: - re_aug=0.8, exp=-0.21, huber(beta=1.0), vel_hi=90, vel_lo=31 - SWA: epochs=80, swa_start=45, swa_lr=5e-4, bn recompute passes=6-8 - Train on train+val merged (710). - feat/targ stats from merged training data. Should I increase epochs for the final / more snapshots? SWA at 80 epochs already stable. I could go a bit longer (epochs=100, swa_start=55) for a touch more snapshot averaging. Marginal. I'll use epochs=100, swa_start=55 for the final to be safe, more snapshots (45). Let me also reconsider vel_hi. Test is up to ~82 (public). Hidden could be slightly higher. vel_hi=90 gives margin to 90. If hidden has a case at 85, still covered. Good. I could bump to 92 for extra safety but 90 is fine; going too high wastes capacity. Keep 90. Should I verify that the merged-data final model behaves well? I can't evaluate extrapolation on merged (no high-vel holdout left). But I trust the holdout-validated method. I WILL sanity check: 1. Predictions on public_test (vel 75-82) , check C_D and C_L are in physically plausible ranges, C_D positive, decreasing-ish with velocity, C_L consistent with aoa. 2. Predictions on val (in-distribution) if I keep a train-only model for checking , but I'm merging. Let me do a two-step: (a) train a train-only SWA model, evaluate on val (in-distribution sanity); (b) train the final on train+val. Actually simpler: evaluate final model on the val samples too (they're in training, so optimistic, but checks sanity/no-NaN). And compare public_test predictions to expectations. Better sanity for extrapolation without labels: check that on public_test the predicted C_D vs aoa and C_L vs aoa relationships match the physics (C_L ≈ 5.68*aoa+0.22, C_D ≈ quadratic in aoa, slightly lower than train due to higher Re). I'll verify predicted C_L correlates ~linearly with aoa with slope ~5.7, and predicted C_D positive and reasonable. Let me write the final training script now. I'll make it self-contained and deterministic. Let me also think: should I run multiple SWA seeds for the final and pick one / or soup them? Since SWA is stable (±0.004), any seed is fine. I'll use seed=0 but could also make a "soup" of 2-3 SWA models from the SAME init... they'd need same init. Not worth it. Single SWA seed=0, but let me pick the seed via val performance among a few to be safe? Val is in-distribution. Hmm. Given SWA stability, I'll just fix seed=0 and maybe train 3 SWA models and average their PREDICTIONS... no, can't ship. Idea for extra robustness shippable in one model: train 3 SWA runs with DIFFERENT seeds, then make a "soup" by averaging their final weights. Even though they start from different inits, SWA solutions might be closer to a shared region? No , different inits → different neuron permutations → averaging breaks. Don't do it. Final: single SWA model, seed=0, trained on train+val. Let me also hold out the possibility of selecting best-of-3-seeds by val mean_nrmse (in-distribution) , but val doesn't capture extrapolation and SWA is stable, so selection gives little. I'll train seed=0 and verify. Actually, to hedge against a bad seed, let me train 3 SWA final models (seeds 0,1,2) on train+val, evaluate EACH on val (in-distribution, optimistic but comparable) AND compute their agreement on public_test, then ship the one wit …[truncated]

SWA is the answer: across 4 seeds it gives **mean_nrmse=0.108 ± 0.004** (std of only 0.004!) with cd=0.153 and bias_cd≈+0.0001 , it matches the best prediction-ensemble while being a single, stable, shippable checkpoint. This confirms SWA + Reynolds augmentation robustly removes the high-velocity drag bias. Now let me write the final training script (train on train+val merged, long SWA run).

bash

cd /app && cat > train_final.py << 'PYEOF'
"""Final model: Reynolds-augmented SWA PointNet trained on train+val.

Physics-grounded design (see analysis):
 - C_L ~ f(shape, aoa), ~velocity-independent.
 - C_D carries a turbulent-skin-friction Reynolds trend C_D ~ V^(-0.21),
   fit from the training data (partial exponent controlling for aoa).
The scored cases sit ABOVE the training inlet-velocity range, so we augment
by resampling velocity across/above that range and rescaling C_D by the
physical law, turning extrapolation into interpolation. SWA yields a single,
low-variance checkpoint. Loss is Huber in standardized target space so both
coefficients are balanced (aligns with per-target NRMSE).
"""
from __future__ import annotations
import json, sys, time
from pathlib import Path
import numpy as np, torch
import torch.nn.functional as F
from torch.optim.swa_utils import AveragedModel

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

CFG=json.load(open(APP/'task_config.json'))

# ---- hyperparameters selected via extrapolation-holdout (train vel<65 -> test vel>=65) ----
RE_AUG=0.8; RE_EXP=-0.21; VEL_LO=31.0; VEL_HI=90.0
EPOCHS=100; SWA_START=55; LR=1e-3; SWA_LR=5e-4; WD=1e-4; BS=32
HUBER_BETA=1.0; BN_PASSES=10; SEED=0

def main():
    torch.set_num_threads(16)
    torch.manual_seed(SEED); np.random.seed(SEED)
    ptr,ttr=load_split(APP/'data'/'train'); pva,tva=load_split(APP/'data'/'val')
    pts=torch.cat([ptr,pva],0); tg=torch.cat([ttr,tva],0)
    N=pts.shape[0]; print(f'[final] training on {N} cases (train+val)',flush=True)
    flat=pts.reshape(-1,4); feat_mean=flat.mean(0); feat_std=flat.std(0).clamp_min(1e-8)
    targ_mean=tg.mean(0); targ_std=tg.std(0).clamp_min(1e-8)
    def standardize(p): return (p-feat_mean.view(1,1,-1))/feat_std.view(1,1,-1)
    def aug(pb,yb):
        m=torch.rand(pb.shape[0])<RE_AUG
        if m.any():
            v_old=pb[m,0,2].clone(); v_new=torch.empty(int(m.sum())).uniform_(VEL_LO,VEL_HI)
            pb[m,:,2]=v_new.view(-1,1); yb[m,0]=yb[m,0]*(v_new/v_old).pow(RE_EXP)
        return pb,yb
    model=build_model(CFG)
    opt=torch.optim.Adam(model.parameters(),lr=LR,weight_decay=WD)
    sched=torch.optim.lr_scheduler.CosineAnnealingLR(opt,T_max=SWA_START)
    swa=AveragedModel(model); nsnap=0; t0=time.time()
    for ep in range(EPOCHS):
        model.train(); idx=torch.randperm(N)
        for s in range(0,N,BS):
            bi=idx[s:s+BS]; pb=pts[bi].clone(); yb=tg[bi].clone()
            pb,yb=aug(pb,yb); xb=standardize(pb); ys=(yb-targ_mean)/targ_std
            opt.zero_grad(set_to_none=True)
            per=F.huber_loss(model(xb),ys,reduction='none',delta=HUBER_BETA)
            per.mean().backward(); opt.step()
        if ep<SWA_START: sched.step()
        else:
            for g in opt.param_groups: g['lr']=SWA_LR
            swa.update_parameters(model); nsnap+=1
    final=swa.module
    print(f'[final] trained {EPOCHS} ep, {nsnap} SWA snapshots, {time.time()-t0:.0f}s',flush=True)
    # recompute BN running stats over augmented training distribution
    for mod in final.modules():
        if isinstance(mod,torch.nn.modules.batchnorm._BatchNorm):
            mod.reset_running_stats(); mod.momentum=None
    final.train()
    with torch.no_grad():
        for _ in range(BN_PASSES):
            idx=torch.randperm(N)
            for s in range(0,N,BS):
                bi=idx[s:s+BS]; pb=pts[bi].clone(); yb=tg[bi].clone()
                pb,_=aug(pb,yb); final(standardize(pb))
    final.eval()
    out=APP/'outputs'; out.mkdir(parents=True,exist_ok=True)
    sd={k:v.cpu() for k,v in final.state_dict().items() if not k.startswith('n_averaged')}
    torch.save({'state_dict':sd,'feat_mean':feat_mean.cpu(),'feat_std':feat_std.cpu(),
                'targ_mean':targ_mean.cpu(),'targ_std':targ_std.cpu()}, out/'model.pt')
    print(f'[final] wrote {out/"model.pt"}',flush=True)

if __name__=='__main__': main()
PYEOF
echo "written train
…[truncated]

output

written train_final.py

Wait, there's a subtlety with AveragedModel and state_dict. AveragedModel wraps the model as `self.module`. `final = swa.module` gives the underlying PointNetRegressor with averaged params. Its state_dict() should match the original architecture keys exactly (encoder.*, head.*). Good. The `n_averaged` buffer belongs to AveragedModel, not module, so final.state_dict() won't have it. My filter is just a safety net. But there's a BN subtlety: AveragedModel by default (use_buffers=False) does NOT average the BN running buffers , `swa.module`'s buffers are whatever they were when AveragedModel was created (copied from initial model) or last synced. Actually, AveragedModel copies the module at init (deepcopy), and update_parameters only updates PARAMETERS (not buffers) when use_buffers=False. So swa.module's BN running_mean/var are stale (from init). That's exactly why I recompute BN stats afterward , which I do. Good. And I reset_running_stats then recompute. But wait: I reset and recompute BN stats on `final` (=swa.module). The parameters are the SWA-averaged weights; BN buffers get recomputed fresh over the data. That's correct SWA+BN procedure. One concern: when I do `final.train()` and forward for BN update, the BN layers with momentum=None accumulate the cumulative average over all batches. With 10 passes * ~23 batches = 230 batches, the running stats converge well. Good. Let me also verify the state_dict keys match what build_model(CFG) produces, so it loads back cleanly. Let me test the full pipeline: run train_final.py, then load the checkpoint into a fresh build_model and run inference on public_test, checking ranges and physics. The training takes ~100 epochs on 710 samples. Earlier 80 epochs on 482 took ~45s. 100 epochs on 710 ~ 45 * (100/80) * (710/482) ≈ 83s, plus BN passes (10 passes * 23 batches forward ≈ small). Total ~100s. Fine within 300s. Let me run it. Actually, before running the final on train+val, let me reconsider one thing: by merging val into training, I lose my ability to sanity check in-distribution generalization on held-out data. But I've already validated the METHOD thoroughly via the vel<65/>=65 holdout. The final merge just adds data. I'll sanity-check the final model's predictions on public_test (physics) and also on the val points (optimistic but catches gross errors/NaNs). Let me run the final training.

A subtlety I've accounted for: `AveragedModel` averages parameters but not BatchNorm buffers, which is exactly why I reset and recompute BN stats afterward over the augmented distribution. Let me verify timing is safe and run the final training.

bash

cd /app && nohup python train_final.py > train_final.log 2>&1 &
echo "started $!"; sleep 150; cat train_final.log

output

started 640
[final] training on 710 cases (train+val)
[final] trained 100 ep, 45 SWA snapshots, 75s
[final] wrote /app/outputs/model.pt

The final model trained in 75s and wrote the checkpoint. Now let me validate: 1. Load the checkpoint into a fresh build_model, confirm it loads with no missing/unexpected keys. 2. Run inference on public_test and check predictions are physically sensible. 3. Check predictions on val (optimistic sanity). 4. Verify the 4 normalization tensors are present with correct shapes. 5. Verify C_D stays positive and shows the expected slightly-lower-than-train behavior at high velocity, and C_L matches ~5.68*aoa+0.22. Let me write a validation script that mimics what the grader does.

The final checkpoint is written (75s). Now the most important step: rigorously validate that it loads cleanly into the exact architecture and produces physically sensible predictions. Let me write a grader-style validation.

bash

cd /app && cat > validate.py << 'PYEOF'
import json, sys, numpy as np, torch
from pathlib import Path
APP=Path('/app'); sys.path.insert(0,str(APP))
from candidate_model import build_model
from dataset import load_split
CFG=json.load(open(APP/'task_config.json'))

ck=torch.load(APP/'outputs'/'model.pt', weights_only=True)
print('checkpoint keys:', list(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)}')

# load exactly as grader would
model=build_model(CFG)
missing,unexpected=model.load_state_dict(ck['state_dict'], strict=False)
print('missing keys:', missing)
print('unexpected keys:', unexpected)
model.eval()
fm,fs=ck['feat_mean'],ck['feat_std']; tm,ts=ck['targ_mean'],ck['targ_std']

def predict(pts):
    x=(pts-fm.view(1,1,-1))/fs.view(1,1,-1)
    with torch.no_grad():
        out=model(x)
    return out*ts+tm

# ---- public_test (HIGH velocity, the scored regime) ----
pt,_=load_split(APP/'data'/'public_test')
pred=predict(pt)
vel=pt[:,0,2].numpy(); aoa=pt[:,0,3].numpy()
cd=pred[:,0].numpy(); cl=pred[:,1].numpy()
print('\n=== public_test predictions (vel %.1f-%.1f) ==='%(vel.min(),vel.max()))
print('C_D: min=%.4f max=%.4f mean=%.4f  (train mean 0.0128)'%(cd.min(),cd.max(),cd.mean()))
print('C_L: min=%.4f max=%.4f mean=%.4f  (train mean 0.660)'%(cl.min(),cl.max(),cl.mean()))
print('any C_D<=0?', bool((cd<=0).any()), ' any NaN?', bool(np.isnan(cd).any() or np.isnan(cl).any()))
# physics checks
print('corr(pred C_L, aoa)=%.3f (expect ~0.94)'%np.corrcoef(cl,aoa)[0,1])
print('corr(pred C_D, aoa^2)=%.3f (expect high)'%np.corrcoef(cd,aoa**2)[0,1])
# C_L slope vs aoa
A=np.array([aoa,np.ones_like(aoa)]).T
sl=np.linalg.lstsq(A,cl,rcond=None)[0]
print('pred C_L ~ aoa slope=%.2f intercept=%.2f (train:5.68,0.23)'%(sl[0],sl[1]))
PYEOF
python validate.py

output

checkpoint keys: ['state_dict', 'feat_mean', 'feat_std', 'targ_mean', 'targ_std']
  feat_mean shape=(4,) vals=[3.83600e-01 7.90000e-03 5.31994e+01 7.68000e-02]
  feat_std shape=(4,) vals=[ 0.3956  0.0333 12.6528  0.0955]
  targ_mean shape=(2,) vals=[0.0128 0.6619]
  targ_std shape=(2,) vals=[0.0051 0.5783]
missing keys: []
unexpected keys: []

=== public_test predictions (vel 75.0-81.6) ===
C_D: min=0.0072 max=0.0258 mean=0.0118  (train mean 0.0128)
C_L: min=-0.5176 max=1.6655 mean=0.6668  (train mean 0.660)
any C_D<=0? False  any NaN? False
corr(pred C_L, aoa)=0.947 (expect ~0.94)
corr(pred C_D, aoa^2)=0.940 (expect high)
pred C_L ~ aoa slope=5.44 intercept=0.26 (train:5.68,0.23)

Excellent validation results: - Checkpoint has all 4 norm tensors with correct shapes. - Loads into the exact architecture with NO missing/unexpected keys. - public_test predictions: - C_D: all positive, min 0.0072, max 0.0258, mean 0.0118. Note train mean C_D was 0.0128. The predicted mean on high-velocity public_test is 0.0118, which is LOWER than train mean , exactly as expected from the Reynolds effect (higher velocity → lower drag). This confirms the model correctly extrapolates the drag-reduction trend. - C_L: range -0.52 to 1.67, mean 0.667, matches train distribution. No NaN. - corr(C_L, aoa)=0.947 (matches physics 0.94). - corr(C_D, aoa^2)=0.940 (high, as expected). - C_L ~ aoa slope 5.44, intercept 0.26 (train 5.68, 0.23) , very close, physically consistent. Everything checks out. The mean C_D being lower (0.0118 vs 0.0128) is the signature of correct Reynolds extrapolation. Let me quantify the drag reduction more precisely and confirm it matches the expected physical magnitude. The public_test mean velocity is 78.2 vs train mean 53. Expected C_D ratio = (78.2/53)^(-0.21) = ? Let me compute: ln(78.2/53)=ln(1.475)=0.389, *(-0.21)=-0.0817, exp=0.9215. So C_D should be ~92% of what it'd be at train velocity for the same shape/aoa. But the public_test aoa distribution might differ slightly from train, so I can't directly compare means. The mean dropped from 0.0128 (train) to 0.0118 (pred at high vel), a factor 0.92 , remarkably consistent with the physics! Though aoa distribution differences also matter. Let me verify by a cleaner test: predict the SAME public_test shapes/aoa but at train-like velocity (53) vs actual (78), and check the C_D ratio matches ~0.92. This is a great sanity check of the velocity response. Let me do it. Also let me run a final robustness check: verify the model's prediction stability and that it behaves monotonically (C_D decreasing) as velocity increases through and beyond the test range. And confirm no pathological behavior at the high end. Let me do a velocity-sweep probe: take public_test points, vary velocity from 50 to 95, plot mean predicted C_D and C_L. Expect C_D to decrease smoothly, C_L ~flat. Let me run these diagnostic probes.

Outstanding , the checkpoint loads into the exact architecture with **zero missing/unexpected keys**, all four norm tensors present, no NaNs, C_D strictly positive. The physics is spot-on: predicted C_L vs aoa slope 5.44 (train 5.68) and **mean C_D dropped to 0.0118 at high velocity vs 0.0128 in training** , the correct Reynolds drag reduction. Let me confirm the model's velocity response quantitatively matches the V^(−0.21) law and is smooth/monotone through the test range.

bash

cd /app && python -c "
import json,sys,numpy as np,torch
from pathlib import Path
APP=Path('/app'); sys.path.insert(0,str(APP))
from candidate_model import build_model
from dataset import load_split
CFG=json.load(open(APP/'task_config.json'))
ck=torch.load(APP/'outputs/model.pt',weights_only=True)
model=build_model(CFG); model.load_state_dict(ck['state_dict']); model.eval()
fm,fs,tm,ts=ck['feat_mean'],ck['feat_std'],ck['targ_mean'],ck['targ_std']
def predict(pts):
    x=(pts-fm.view(1,1,-1))/fs.view(1,1,-1)
    with torch.no_grad(): out=model(x)
    return out*ts+tm
pt,_=load_split(APP/'data/public_test')
# velocity sweep on the SAME shapes/aoa
print('vel  meanC_D   meanC_L   (C_D ratio vs v=53)')
base=None
for v in [50,55,60,65,70,75,78,82,86,90,95]:
    p=pt.clone(); p[:,:,2]=float(v)
    pr=predict(p); mcd=pr[:,0].mean().item(); mcl=pr[:,1].mean().item()
    if v==50: base=mcd
    print('%3d  %.5f  %.4f   %.3f'%(v,mcd,mcl,mcd/base))
# compare to physical law (v/50)^-0.21
print()
for v in [78,90]:
    print('physical (v=%d/50)^-0.21 = %.3f'%(v,(v/50.)**-0.21))
"

output

vel  meanC_D   meanC_L   (C_D ratio vs v=53)
 50  0.01299  0.6654   1.000
 55  0.01274  0.6649   0.980
 60  0.01251  0.6645   0.963
 65  0.01230  0.6651   0.947
 70  0.01211  0.6663   0.932
 75  0.01193  0.6669   0.918
 78  0.01182  0.6668   0.909
 82  0.01168  0.6666   0.899
 86  0.01157  0.6669   0.890
 90  0.01149  0.6683   0.884
 95  0.01145  0.6728   0.882

physical (v=78/50)^-0.21 = 0.911
physical (v=90/50)^-0.21 = 0.884

This is a near-perfect match to the physical law: - At v=78: model C_D ratio = 0.909, physical law (78/50)^-0.21 = 0.911. - At v=90: model ratio = 0.884, physical = 0.884. EXACT match! - C_D decreases smoothly and monotonically with velocity through and beyond the test range (no pathological behavior up to 95). - C_L stays essentially flat (0.665-0.673) across all velocities , correctly velocity-independent. The model has learned EXACTLY the physical Reynolds scaling C_D ∝ V^(-0.21) and applies it smoothly into and beyond the extrapolation regime. This is exactly what I wanted. The velocity augmentation converted extrapolation into interpolation, and the model generalizes the drag-reduction law correctly. This is a very strong result. The model: 1. Loads into the exact architecture (contract satisfied). 2. Has all 4 normalization tensors. 3. Predicts physically sensible C_D (positive, decreasing with velocity per V^-0.21) and C_L (linear in aoa, velocity-independent). 4. On the extrapolation-holdout proxy, achieves mean_nrmse ~0.108 (vs baseline 0.163), with the C_D high-velocity bias essentially eliminated. Let me do a final sanity comparison against the provided baseline trainer to quantify the improvement on the holdout, and also confirm the final checkpoint works through a clean-room inference one more time. Actually I've already confirmed loading and inference. Let me also double check: the task says targets are [C_D, C_L]. My targ_mean = [0.0128, 0.6619] , yes, index 0 is C_D (~0.0128), index 1 is C_L (~0.66). Correct order. And feature channels: feat_mean=[0.384, 0.0079, 53.2, 0.0768] = [x, y, vel, aoa]. Correct. Let me make one final consideration: robustness of the choice to merge val into training. I validated the method on the holdout using train-only (vel<65). The final uses train+val (all velocities up to 75). The method is identical. The merge adds 80 more samples (more shapes/conditions), which should only help. I'm confident. Let me also verify the model handles the exact public_test tensor as-is (I did). And let me make sure there are no issues with the checkpoint being loaded with weights_only=True (it's a plain dict of tensors, so fine). One more: Let me confirm the state_dict keys exactly match a fresh model's keys (strict=True load). I loaded with strict=False and got no missing/unexpected, which means strict=True would also succeed. But let me explicitly test strict=True to be 100% sure the grader (which may use strict=True) won't fail. Let me run a final strict-load test and also compare to baseline quickly on the holdout to document improvement. Actually, I'll just do the strict-load test and a clean final inference, to conserve tokens. Let me also print parameter count / confirm architecture untouched. Let me also reconsider: is there any risk the grader uses a different normalization convention, e.g., expects feat_std to not be clamped or something? No, my standardization is standard (subtract mean, divide std). The task explicitly says feat_mean/feat_std standardize features before forward, and targ_mean/targ_std map outputs back. I do exactly pred = out*targ_std + targ_mean, and x=(pts-feat_mean)/feat_std. This matches the described contract. Let me do the strict load test.

The match to physics is essentially exact: at v=78 the model's C_D ratio is **0.909 vs the theoretical (78/50)^−0.21 = 0.911**, and at v=90 it's **0.884 vs 0.884** , the model learned the Reynolds drag law precisely and applies it smoothly and monotonically through and beyond the scored regime, while C_L stays correctly velocity-flat. Let me do a final strict-load test (in case the grader uses `strict=True`) and confirm the improvement over the provided baseline on the holdout.

bash

cd /app && python -c "
import json,sys,torch
from pathlib import Path
APP=Path('/app'); sys.path.insert(0,str(APP))
from candidate_model import build_model
CFG=json.load(open(APP/'task_config.json'))
ck=torch.load(APP/'outputs/model.pt',weights_only=True)
m=build_model(CFG)
m.load_state_dict(ck['state_dict'], strict=True)   # must not raise
print('STRICT load OK; param tensors:', len(ck['state_dict']))
print('total params:', sum(p.numel() for p in m.parameters()))
import os; print('checkpoint size (KB): %.1f'%(os.path.getsize(APP/'outputs/model.pt')/1024))
"

output

STRICT load OK; param tensors: 27
total params: 83842
checkpoint size (KB): 338.5

Strict load works. 83842 params (the fixed architecture). Checkpoint 338 KB. All good. Now let me do a final documented comparison vs the provided baseline trainer on the extrapolation holdout, to quantify the improvement. I'll run the baseline approach (plain MSE, no aug, 40 epochs as in the default config) vs my method on the holdout. Actually I already have baseline numbers (mean_nrmse 0.163 with huber/60ep; the original train.py uses MSE/40ep which would be similar or worse). My method gets ~0.108. That's a solid ~34% improvement on the proxy. Let me do a clean final head-to-head on the holdout: - baseline = exact train.py recipe (MSE, 40 epochs, no aug, Adam lr1e-3 wd1e-4) - mine = SWA + Reynolds aug using the SAME train vel<65 / test vel>=65 split, reporting mean_nrmse, cd, cl, bias. This documents the gain cleanly. Let me run it (a few seeds for stability), in the background. Actually, I want to be efficient with tokens/time. I've already thoroughly validated. Let me do one concise head-to-head (3 seeds each) to document, running in background, and meanwhile finalize. Let me run it.

Strict load succeeds (27 tensors, 83,842 params , the untouched architecture). Let me run one concise, documented head-to-head on the extrapolation holdout: the exact provided baseline recipe (plain MSE, 40 epochs, no augmentation) versus my method (Reynolds-augmented SWA), averaged over 3 seeds.

bash

cd /app && cat > headtohead.py << 'PYEOF'
import numpy as np, torch
from exp import get_data, train_eval, train_swa_eval
pts,tg=get_data(); vel=pts[:,0,2]; tr=vel<65; va=vel>=65
Xtr,Ytr,Xva,Yva=pts[tr],tg[tr],pts[va],tg[va]
torch.set_num_threads(16)
def avg(fn,**kw):
    res=[fn(Xtr,Ytr,Xva,Yva,seed=s,**kw) for s in range(3)]
    return {k:np.mean([r[k] for r in res]) for k in res[0]}
# exact provided-baseline recipe: plain MSE, 40 epochs, no aug, constant LR
b=avg(train_eval, epochs=40, loss='mse', re_aug=0.0, sched='none')
print('BASELINE (MSE,40ep,no aug): mean=%.4f cd=%.4f cl=%.4f bias_cd=%+.5f'%(b['mean_nrmse'],b['nrmse_cd'],b['nrmse_cl'],b['bias_cd']),flush=True)
# my method: Reynolds-aug SWA
m=avg(train_swa_eval, epochs=100, swa_start=55, swa_lr=5e-4, re_aug=0.8, re_exp=-0.21, loss='huber', huber_beta=1.0, vel_hi=90.0, bn_update_passes=10)
print('OURS (ReynoldsAug+SWA):     mean=%.4f cd=%.4f cl=%.4f bias_cd=%+.5f'%(m['mean_nrmse'],m['nrmse_cd'],m['nrmse_cl'],m['bias_cd']),flush=True)
print('improvement: mean %.1f%%  cd %.1f%%'%(100*(1-m['mean_nrmse']/b['mean_nrmse']),100*(1-m['nrmse_cd']/b['nrmse_cd'])),flush=True)
print('DONE',flush=True)
PYEOF
nohup python headtohead.py > headtohead.log 2>&1 &
echo "started $!"; sleep 200; cat headtohead.log

output

started 793
BASELINE (MSE,40ep,no aug): mean=0.3157 cd=0.4335 cl=0.1979 bias_cd=+0.00124

The baseline (exact provided recipe: MSE, 40 epochs, no aug, constant LR) on the extrapolation holdout gives mean_nrmse=0.316, cd=0.434, cl=0.198, bias_cd=+0.00124. That's much worse than my earlier "baseline" which used Huber+cosine+60ep (0.163). The original train.py recipe (MSE/40ep/constant) is notably worse, especially on C_D (0.434) with a large positive bias (+0.00124) , exactly the "over-predict drag at high velocity" failure. Now let me wait for OURS to finish.

The provided baseline recipe (MSE, 40 epochs, no aug) is notably worse on the extrapolation holdout: **mean_nrmse=0.316, cd=0.434, with a large positive drag bias (+0.00124)** , the classic over-predict-drag-at-high-speed failure. Let me get my method's numbers.

bash

cd /app && sleep 140 && cat headtohead.log

output

BASELINE (MSE,40ep,no aug): mean=0.3157 cd=0.4335 cl=0.1979 bias_cd=+0.00124
OURS (ReynoldsAug+SWA):     mean=0.1099 cd=0.1615 cl=0.0583 bias_cd=+0.00016
improvement: mean 65.2%  cd 62.7%
DONE

The head-to-head on the extrapolation holdout: - BASELINE (exact provided recipe): mean_nrmse=0.316, cd=0.434, cl=0.198, bias_cd=+0.00124 - OURS (Reynolds-aug + SWA): mean_nrmse=0.110, cd=0.162, cl=0.058, bias_cd=+0.00016 Improvement: 65.2% lower mean_nrmse, 62.7% lower C_D NRMSE. The drag bias is cut ~8x. Both targets improved substantially (C_L also 0.198→0.058, a 70% reduction, thanks to more effective data via augmentation + SWA stabilization). This is a strong, well-documented result on a proxy that directly simulates the real task (train low velocity → test high velocity). My final model is already trained and saved at /app/outputs/model.pt, validated: - Loads strictly into the exact architecture. - All 4 normalization tensors present and correct. - Physically sensible, correctly extrapolating predictions. Let me do a final cleanup and a last confirmation that the saved model.pt is the final one (trained on train+val with the chosen config). The final model was trained by train_final.py earlier (the one I validated). The headtohead used a separate holdout (didn't touch outputs/model.pt). So outputs/model.pt is intact from train_final.py. Let me re-verify the file is present and re-run the quick validation once more to be certain nothing overwrote it. Actually, headtohead.py imports exp which doesn't write model.pt, and train_final wrote it. Nothing else wrote to outputs. Let me just confirm the file timestamp and re-validate quickly. Let me do a final confirmation and then summarize. Let me also clean up scratch files? Not necessary, but I'll leave them; they don't interfere. The deliverable is /app/outputs/model.pt. Let me do a final verification of the output file.

The head-to-head is conclusive on the extrapolation proxy (train low-velocity → test high-velocity): | Method | mean_nrmse | C_D nrmse | C_L nrmse | C_D bias | |---|---|---|---|---| | Provided baseline (MSE, 40ep, no aug) | 0.316 | 0.434 | 0.198 | +0.00124 | | **Ours (Reynolds-aug + SWA)** | **0.110** | **0.162** | **0.058** | **+0.00016** | That's a **65% reduction in mean NRMSE**, 63% on drag, with the high-speed drag bias cut ~8×, and lift also improved ~70%. Let me do a final confirmation that the deliverable is intact and correct.

bash

cd /app && ls -la outputs/ && echo "--- final integrity re-check ---" && python -c "
import json,sys,torch,numpy as np
from pathlib import Path
APP=Path('/app'); sys.path.insert(0,str(APP))
from candidate_model import build_model
from dataset import load_split
CFG=json.load(open(APP/'task_config.json'))
ck=torch.load(APP/'outputs/model.pt',weights_only=True)
assert set(ck)>= {'state_dict','feat_mean','feat_std','targ_mean','targ_std'}
for k in ['feat_mean','feat_std','targ_mean','targ_std']:
    t=ck[k]; assert t.shape[0]==(4 if 'feat' in k else 2) and torch.isfinite(t).all()
m=build_model(CFG); m.load_state_dict(ck['state_dict'],strict=True); m.eval()
# full inference on public_test end-to-end
pt,_=load_split(APP/'data/public_test')
x=(pt-ck['feat_mean'].view(1,1,-1))/ck['feat_std'].view(1,1,-1)
with torch.no_grad(): out=m(x)*ck['targ_std']+ck['targ_mean']
print('OK: strict-load + inference clean. pred shape',tuple(out.shape),
      '| C_D>0:',bool((out[:,0]>0).all()),'| finite:',bool(torch.isfinite(out).all()))
print('C_D mean=%.4f (high-vel, <train 0.0128) | C_L mean=%.3f'%(out[:,0].mean(),out[:,1].mean()))
"

output

total 347
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 19 22:10 .
drwxrws--- 5 h2tagent h2tagent   4096 Jun 19 22:12 ..
-rw-r--r-- 1 h2tagent h2tagent 346611 Jun 19 22:10 model.pt
--- final integrity re-check ---
OK: strict-load + inference clean. pred shape (100, 2) | C_D>0: True | finite: True
C_D mean=0.0118 (high-vel, <train 0.0128) | C_L mean=0.667

The deliverable is complete and verified. Here's a summary of what I did and why. ## Outcome `/app/outputs/model.pt` is written and validated: it contains `state_dict` plus all four normalization tensors (`feat_mean`/`feat_std` length‑4, `targ_mean`/`targ_std` length‑2), loads into the fixed PointNet with **`strict=True` and zero missing/unexpected keys**, and produces clean, finite, physically sensible predictions on the high‑velocity scored regime. ## Key insight: the task is really a drag-Reynolds extrapolation problem I diagnosed the distribution shift directly from the data: training inlet velocities span 31–75 m/s while the scored cases sit at 75–82 m/s. Analyzing the physics in the labels showed: - **C_L** depends almost entirely on angle of attack (corr 0.94) and is **velocity-independent** , so it extrapolates safely. - **C_D** carries a real Reynolds trend. A log–log partial fit (controlling for aoa) gave an exponent of **−0.21**, i.e. C_D ∝ V^(−0.21) , the classic turbulent skin-friction scaling. Ignoring it makes a model systematically **over-predict drag** at high speed (I measured a +0.00124 bias in the baseline). ## What I changed about training (the architecture was left untouched) - **Physics-grounded velocity augmentation**: I resample each case's velocity across/above the full range (up to 90 m/s) and rescale its C_D by (v′/v)^(−0.21), leaving C_L fixed. This turns test-time extrapolation into interpolation. - **Huber loss in standardized target space**, which balances both coefficients (aligning with per-target NRMSE) and is far more robust than MSE here (MSE gave C_D nrmse 0.30 vs 0.16). - **SWA** to produce a single, low-variance checkpoint (the only shippable form, since the grader loads one `state_dict`), with BatchNorm statistics recomputed afterward. - Trained the final model on **train+val merged** (710 cases) for more data. ## How I validated (rather than guessing) I built an **extrapolation proxy** , train on vel<65, test on vel≥65 , to mimic the real low→high shift, and used it to select every choice. Against the exact provided baseline recipe on that proxy, the final method cut **mean NRMSE by 65%** (0.316→0.110), **drag NRMSE by 63%**, and the high-speed drag bias ~8×. A direct velocity sweep confirmed the trained model reproduces the C_D ∝ V^(−0.21) law almost exactly (ratio 0.909 vs theoretical 0.911 at 78 m/s; 0.884 vs 0.884 at 90 m/s), smoothly and monotonically, while keeping C_L flat in velocity. One caveat worth stating plainly: my proxy tops out at 75 m/s, so it under-samples the very top of the scored range, and the −0.21 law is a global approximation (friction drag follows it more cleanly than pressure drag). The augmentation up to 90 m/s and the exact match on the velocity sweep give me confidence this holds across the scored band, but the true test distribution remains unobserved.

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: 2  status: completed  raw_score: 0.38
# reward rule: continuous: score 0.38 < 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).
----------------------------------------------------------------------
total 347
drwxr-sr-x 2 h2tagent h2tagent   4096 Jun 19 22:10 .
drwxrws--- 5 h2tagent h2tagent   4096 Jun 19 22:12 ..
-rw-r--r-- 1 h2tagent h2tagent 346611 Jun 19 22:10 model.pt
--- final integrity re-check ---
OK: strict-load + inference clean. pred shape (100, 2) | C_D>0: True | finite: True
C_D mean=0.0118 (high-vel, <train 0.0128) | C_L mean=0.667

Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_c8d4433af7b34581. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_c8d4433af7b34581 · verifier authoritative; classifier explanatory.