src/plot_t2_graybox_closure.py

"""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()