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:
| """ | |
| 01 — Train the canonical POD-NN surrogate. | |
| Trains on the first TRAIN_SIZE=100 samples, holds out the last | |
| TEST_SIZE=400 for evaluation, retains N_MODES=4 POD modes. | |
| Saves trained artifacts to 2_AnalyzedData/trained_model/ for use by | |
| the plotting scripts (02, 05, 07, 08). | |
| """ | |
| from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error | |
| from _utils import ( | |
| load_data, train_pod_nn, predict_field, save_artifacts, seed_all, | |
| TRAIN_SIZE, TEST_SIZE, N_MODES, EPOCHS, | |
| ) | |
| def main(): | |
| seed_all() | |
| print(f"Training POD-NN | N_train={TRAIN_SIZE} N_test={TEST_SIZE} " | |
| f"r={N_MODES} epochs<={EPOCHS}") | |
| parameters, fields, _, _ = load_data() | |
| X_train = parameters[:TRAIN_SIZE] | |
| Y_train = fields[:TRAIN_SIZE] | |
| X_test = parameters[-TEST_SIZE:] | |
| Y_test = fields[-TEST_SIZE:] | |
| trained = train_pod_nn(X_train, Y_train, n_modes=N_MODES, epochs=EPOCHS) | |
| var = trained["pca"].explained_variance_ratio_.sum() * 100 | |
| print(f" POD variance explained: {var:.2f}%") | |
| print(f" Stopped at epoch: {len(trained['history']['loss'])}") | |
| Y_pred = predict_field(trained, X_test) | |
| r2 = r2_score(Y_test.flatten(), Y_pred.flatten()) | |
| rmse = mean_squared_error(Y_test.flatten(), Y_pred.flatten()) ** 0.5 | |
| mae = mean_absolute_error(Y_test.flatten(), Y_pred.flatten()) | |
| print(f"\nTest set metrics ({TEST_SIZE} samples):") | |
| print(f" R2 = {r2:.6f}") | |
| print(f" RMSE = {rmse:.4f} K") | |
| print(f" MAE = {mae:.4f} K") | |
| save_artifacts(trained) | |
| if __name__ == "__main__": | |
| main() | |