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:
| """ | |
| 07 — Single-sample prediction comparison plots. | |
| Loads the canonical model, picks 5 evenly spaced indices from the | |
| held-out test set, generates one figure per sample with three panels | |
| (prediction / truth / abs-error) and the input parameters listed. | |
| """ | |
| import time | |
| 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, | |
| ) | |
| N_PLOTS = 5 | |
| DISPLAY = { | |
| "chip1.thermal.heat_flux": ("Chip 1 Heat Flux", "W/m²", ".0f"), | |
| "chip2.thermal.heat_flux": ("Chip 2 Heat Flux", "W/m²", ".0f"), | |
| "inlet1.momentum.mass_flow_rate": ("Inlet 1 Mass Flow", "kg/s", ".6f"), | |
| "inlet1.thermal.total_temperature": ("Inlet 1 Temperature", "K", ".1f"), | |
| "inlet2.momentum.mass_flow_rate": ("Inlet 2 Mass Flow", "kg/s", ".6f"), | |
| "inlet2.thermal.total_temperature": ("Inlet 2 Temperature", "K", ".1f"), | |
| } | |
| def plot_one(coords, y_pred, y_true, idx, t_inf_ms, x_vals, p_names, out_dir): | |
| FS = 16 | |
| y_mm, z_mm = yz_mm(coords) | |
| abs_err = np.abs(y_pred - y_true) | |
| vmin = min(y_true.min(), y_pred.min()) | |
| vmax = max(y_true.max(), y_pred.max()) | |
| fig = plt.figure(figsize=(14, 12)) | |
| gs = fig.add_gridspec(3, 2, width_ratios=[3, 1], wspace=0.05, hspace=0.15) | |
| ax_p, ax_t, ax_e = (fig.add_subplot(gs[i, 0]) for i in range(3)) | |
| ax_text = fig.add_subplot(gs[:, 1]) | |
| for ax, vals, title, cmap, lbl, kw in [ | |
| (ax_p, y_pred, "Prediction", "jet", "T (K)", dict(vmin=vmin, vmax=vmax)), | |
| (ax_t, y_true, "Truth", "jet", "T (K)", dict(vmin=vmin, vmax=vmax)), | |
| (ax_e, abs_err, "Absolute Error", "hot", "|Error| (K)", dict()), | |
| ]: | |
| sc = ax.scatter(z_mm, y_mm, c=vals, s=1, cmap=cmap, **kw) | |
| ax.set_xlabel("Z (mm)", fontsize=FS) | |
| ax.set_ylabel("Y (mm)", fontsize=FS) | |
| ax.set_title(title, fontsize=FS) | |
| ax.tick_params(labelsize=FS - 4) | |
| ax.set_aspect("equal") | |
| cb = plt.colorbar(sc, ax=ax, orientation="vertical", shrink=0.8) | |
| cb.set_label(lbl, fontsize=FS - 2) | |
| cb.ax.tick_params(labelsize=FS - 4) | |
| ax_text.axis("off") | |
| r2 = r2_score(y_true, y_pred) | |
| mae = mean_absolute_error(y_true, y_pred) | |
| lines = [f"Test Sample {idx}", "", "Input Parameters", "-" * 28] | |
| for name, v in zip(p_names, x_vals): | |
| if name in DISPLAY: | |
| lbl, unit, fmt = DISPLAY[name] | |
| lines.append(f"{lbl}:") | |
| lines.append(f" {v:{fmt}} {unit}") | |
| else: | |
| lines.append(f"{name}: {v:.6g}") | |
| lines += ["", "Metrics", "-" * 28, | |
| f"R²: {r2:.6f}", | |
| f"MAE: {mae:.3f} K", | |
| f"Max AE: {abs_err.max():.3f} K", | |
| f"Inference: {t_inf_ms:.2f} ms"] | |
| ax_text.text(0.05, 0.95, "\n".join(lines), | |
| transform=ax_text.transAxes, fontsize=FS - 4, | |
| verticalalignment="top", fontfamily="monospace") | |
| plt.tight_layout() | |
| out = out_dir / f"comparison_sample_{idx:04d}.png" | |
| plt.savefig(out, dpi=300, bbox_inches="tight") | |
| print(f" Saved {out.name}") | |
| plt.close() | |
| def main(): | |
| parameters, fields, coords, p_names = load_data() | |
| X_test = parameters[-TEST_SIZE:] | |
| Y_test = fields[-TEST_SIZE:] | |
| trained = load_artifacts() | |
| out_dir = PLOTS_DIR / "single_predictions" | |
| out_dir.mkdir(parents=True, exist_ok=True) | |
| indices = np.linspace(0, TEST_SIZE - 1, N_PLOTS, dtype=int) | |
| for idx in indices: | |
| x = X_test[idx:idx+1] | |
| t0 = time.perf_counter() | |
| y_pred = predict_field(trained, x)[0] | |
| t_inf_ms = (time.perf_counter() - t0) * 1000 | |
| plot_one(coords, y_pred, Y_test[idx], int(idx), | |
| t_inf_ms, X_test[idx], p_names, out_dir) | |
| if __name__ == "__main__": | |
| main() | |