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Tasks:
Tabular Regression
Languages:
English
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1K<n<10K
ArXiv:
Tags:
computational-fluid-dynamics
surrogate-modeling
proper-orthogonal-decomposition
electronics-cooling
thermal-management
reduced-order-model
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File size: 1,571 Bytes
9856aea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | """
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()
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