Datasets:
Tasks:
Tabular Regression
Languages:
English
Size:
1K<n<10K
ArXiv:
Tags:
computational-fluid-dynamics
surrogate-modeling
proper-orthogonal-decomposition
electronics-cooling
thermal-management
reduced-order-model
License:
| """ | |
| 08 — Mean absolute error per spatial point across the test set. | |
| Loads the canonical model, runs inference on all 400 held-out test | |
| samples, computes |error| at every spatial point per sample, averages | |
| across the test set, plots the resulting MAE field on the yz-mid plane. | |
| """ | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn.metrics import r2_score, mean_absolute_error | |
| from _utils import ( | |
| load_data, load_artifacts, predict_field, yz_mm, | |
| TEST_SIZE, PLOTS_DIR, | |
| ) | |
| def main(): | |
| parameters, fields, coords, _ = load_data() | |
| X_test = parameters[-TEST_SIZE:] | |
| Y_test = fields[-TEST_SIZE:] | |
| trained = load_artifacts() | |
| Y_pred = predict_field(trained, X_test) | |
| abs_err = np.abs(Y_pred - Y_test) # (400, n_pts) | |
| mean_err = abs_err.mean(axis=0) # (n_pts,) | |
| r2_all = r2_score(Y_test.flatten(), Y_pred.flatten()) | |
| mae_all = mean_absolute_error(Y_test.flatten(), Y_pred.flatten()) | |
| print(f"Test set: R²={r2_all:.6f} MAE={mae_all:.4f} K " | |
| f"MaxAE={abs_err.max():.4f} K") | |
| print(f"MAE field: mean={mean_err.mean():.4f} K " | |
| f"max={mean_err.max():.4f} K min={mean_err.min():.4f} K") | |
| y_mm, z_mm = yz_mm(coords) | |
| FS = 20 | |
| fig, ax = plt.subplots(figsize=(10, 6)) | |
| sc = ax.scatter(z_mm, y_mm, c=mean_err, s=1, cmap="hot") | |
| ax.set_xlabel("Z (mm)", fontsize=FS) | |
| ax.set_ylabel("Y (mm)", fontsize=FS) | |
| ax.set_aspect("equal") | |
| ax.tick_params(labelsize=FS - 4) | |
| cb = plt.colorbar(sc, ax=ax, orientation="vertical", shrink=0.5) | |
| cb.set_label("Mean |Error| (K)", fontsize=FS - 2) | |
| cb.ax.tick_params(labelsize=FS - 4) | |
| plt.tight_layout() | |
| PLOTS_DIR.mkdir(parents=True, exist_ok=True) | |
| out = PLOTS_DIR / "mean_error_field.png" | |
| plt.savefig(out, dpi=300, bbox_inches="tight") | |
| print(f"Saved {out}") | |
| plt.close() | |
| if __name__ == "__main__": | |
| main() | |