| """Create compact paper-ready figures for the incomplete-physics closure.""" |
|
|
| from __future__ import annotations |
|
|
| import json |
| from pathlib import Path |
|
|
| import matplotlib |
|
|
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import numpy as np |
| import torch |
|
|
| from src import t2_graybox_discrete_energy as graybox |
| from src.generate_t2_graybox_closure import MATERIAL |
| from src.train_t2_graybox_discrete_energy import load_cohort |
| from src.train_t2_multiaxial_models import RecurrentStress |
|
|
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| DATA = ROOT / "data" / "t2_graybox_closure_v1" / "cohort.h5" |
| MODELS = ROOT / "models" / "t2_graybox_closure_v1" |
| ARTIFACTS = ROOT / "artifacts" / "t2_graybox_closure_v1" |
|
|
|
|
| def main() -> None: |
| cohort = load_cohort(DATA) |
| denrm_checkpoint = torch.load(MODELS / "denrm.pt", map_location="cpu", weights_only=False) |
| denrm = graybox.NeuralHardeningLaw(channels=2) |
| denrm.load_state_dict(denrm_checkpoint["state_dict"]) |
| denrm.eval() |
| gru_checkpoint = torch.load(MODELS / "gru.pt", map_location="cpu", weights_only=False) |
| gru = RecurrentStress(6, cell="gru", hidden=72) |
| gru.load_state_dict(gru_checkpoint["state_dict"]) |
| gru.eval() |
| norm = gru_checkpoint["normalization"] |
| with torch.no_grad(): |
| denrm_stress = graybox.rollout( |
| cohort.strain, |
| torch.full((len(cohort.strain),), float(MATERIAL["young_pa"])), |
| torch.full((len(cohort.strain),), float(MATERIAL["poisson"])), |
| torch.full((len(cohort.strain),), float(MATERIAL["yield_stress_pa"])), |
| denrm, |
| bisection_iterations=24, |
| )["stress"] |
| gru_stress = ( |
| gru((cohort.strain - norm["strain_mean"]) / norm["strain_std"]) |
| * norm["stress_std"] |
| + norm["stress_mean"] |
| ) |
|
|
| metrics = json.loads((ARTIFACTS / "model_metrics.json").read_text()) |
| figure, axes = plt.subplots(2, 2, figsize=(11.0, 7.8), constrained_layout=True) |
| colors = {"truth": "#1f2937", "denrm": "#e11d48", "gru": "#2563eb"} |
| component_labels = ("xx", "yy", "zz", "xy", "yz", "xz") |
| for axis, family in zip( |
| axes[0], ("out_of_phase_lissajous", "random_direction_blocks"), strict=True |
| ): |
| index = next(i for i, value in enumerate(cohort.families) if value == family) |
| strain = cohort.strain[index].numpy() |
| component = int(np.argmax(np.ptp(strain, axis=0))) |
| x = 100.0 * strain[:, component] |
| axis.plot( |
| x, |
| cohort.stress[index, :, component].numpy() / 1.0e6, |
| color=colors["truth"], |
| linewidth=2.1, |
| label="AgentFEM reference", |
| ) |
| axis.plot( |
| x, |
| denrm_stress[index, :, component].numpy() / 1.0e6, |
| "--", |
| color=colors["denrm"], |
| linewidth=1.8, |
| label="DENIM (2-state closure)", |
| ) |
| axis.plot( |
| x, |
| gru_stress[index, :, component].numpy() / 1.0e6, |
| color=colors["gru"], |
| linewidth=1.1, |
| alpha=0.85, |
| label="GRU", |
| ) |
| axis.set_title(family.replace("_", " ")) |
| axis.set_xlabel(f"strain {component_labels[component]} (%)") |
| axis.set_ylabel(f"stress {component_labels[component]} (MPa)") |
| axis.grid(alpha=0.22) |
| axes[0, 0].legend(frameon=False, fontsize=8) |
|
|
| names = ("Incomplete J2", "GRU", "DENIM") |
| keys = ("incomplete_j2", "gru", "denrm") |
| rmse = [metrics["models"][key]["test"]["rmse_mpa"] for key in keys] |
| bars = axes[1, 0].bar( |
| names, rmse, color=("#9ca3af", colors["gru"], colors["denrm"]) |
| ) |
| axes[1, 0].bar_label(bars, fmt="%.2f") |
| axes[1, 0].set_ylabel("held-out path RMSE (MPa)") |
| axes[1, 0].set_title("unseen loading-path accuracy") |
| axes[1, 0].grid(axis="y", alpha=0.22) |
|
|
| peeq = np.linspace(0.0, 0.10, 250) |
| with torch.no_grad(): |
| learned = denrm.isotropic( |
| torch.tensor(peeq, dtype=torch.float32), |
| torch.full((len(peeq),), float(MATERIAL["yield_stress_pa"])), |
| ).numpy() |
| reference = np.interp( |
| peeq, |
| np.asarray(MATERIAL["hardening_peeq"]), |
| np.asarray(MATERIAL["hardening_stress_pa"]), |
| ) - float(MATERIAL["yield_stress_pa"]) |
| axes[1, 1].plot(peeq, reference / 1.0e6, color=colors["truth"], linewidth=2.1, label="hidden table") |
| axes[1, 1].plot(peeq, learned / 1.0e6, "--", color=colors["denrm"], linewidth=1.8, label="learned monotone law") |
| axes[1, 1].set_xlabel("equivalent plastic strain") |
| axes[1, 1].set_ylabel("isotropic hardening radius (MPa)") |
| axes[1, 1].set_title("unknown hardening-law recovery") |
| axes[1, 1].grid(alpha=0.22) |
| axes[1, 1].legend(frameon=False, fontsize=8) |
|
|
| figure.suptitle( |
| "Incomplete-physics closure: 3-state tabulated reference → 2-state DENIM", |
| fontsize=13, |
| ) |
| ARTIFACTS.mkdir(parents=True, exist_ok=True) |
| figure.savefig(ARTIFACTS / "closure_summary.png", dpi=220) |
| plt.close(figure) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
