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ebc46a9 | 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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 | """Train pointwise-MLP and history-aware GRU baselines for T2 v1."""
from __future__ import annotations
import argparse
import json
import time
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
try:
from src.load_t2_material_loading_memory import iter_trajectories
except ModuleNotFoundError:
from load_t2_material_loading_memory import iter_trajectories
ROOT = Path(__file__).resolve().parents[1]
DATA_DIR = ROOT / "data" / "t2_material_loading_memory_v1"
ARTIFACT_DIR = ROOT / "artifacts" / "t2_material_loading_memory_v1"
PARAMETER_NAMES = (
"young_pa",
"poisson",
"yield_stress_pa",
"hardening_modulus_pa",
"backstress_c1_pa",
"backstress_gamma1",
"backstress_c2_pa",
"backstress_gamma2",
"isotropic_saturation_pa",
"isotropic_rate",
)
MODEL_NAMES = ("j2_linear_isotropic", "chaboche_combined")
PATH_FAMILIES = (
"monotonic_tension",
"unload_reload",
"tension_compression",
"symmetric_cyclic",
"mean_shifted_cyclic",
"variable_amplitude",
)
class PointwiseMLP(nn.Module):
def __init__(self, input_size: int) -> None:
super().__init__()
self.network = nn.Sequential(
nn.Linear(input_size, 64),
nn.Tanh(),
nn.Linear(64, 64),
nn.Tanh(),
nn.Linear(64, 1),
)
def forward(self, values: torch.Tensor) -> torch.Tensor:
return self.network(values)
class HistoryGRU(nn.Module):
def __init__(self, input_size: int) -> None:
super().__init__()
self.recurrent = nn.GRU(input_size, 64, batch_first=True)
self.output = nn.Linear(64, 1)
def forward(self, values: torch.Tensor) -> torch.Tensor:
hidden, _ = self.recurrent(values)
return self.output(hidden)
def _load_arrays() -> dict[str, object]:
samples = list(iter_trajectories(DATA_DIR))
parameters = np.asarray(
[[float(sample["parameters"][name]) for name in PARAMETER_NAMES] for sample in samples],
dtype=np.float64,
)
model_one_hot = np.zeros((len(samples), len(MODEL_NAMES)), dtype=np.float64)
for index, sample in enumerate(samples):
model_one_hot[index, MODEL_NAMES.index(sample["material_model"])] = 1.0
strain = np.asarray(
[sample["history"]["signed_equivalent_strain"] for sample in samples],
dtype=np.float64,
)
stress = np.asarray(
[sample["history"]["signed_equivalent_stress_pa"] for sample in samples],
dtype=np.float64,
)
splits = np.asarray([sample["split"] for sample in samples])
train = splits == "train"
parameter_mean = parameters[train].mean(axis=0)
parameter_scale = parameters[train].std(axis=0)
parameter_scale[parameter_scale < 1.0e-15] = 1.0
normalized_parameters = (parameters - parameter_mean) / parameter_scale
strain_scale = float(np.max(np.abs(strain[train])))
normalized_strain = strain / strain_scale
delta = np.diff(normalized_strain, axis=1, prepend=normalized_strain[:, :1])
constant = np.concatenate((normalized_parameters, model_one_hot), axis=1)
repeated = np.repeat(constant[:, None, :], strain.shape[1], axis=1)
pointwise = np.concatenate((normalized_strain[:, :, None], repeated), axis=2)
history = np.concatenate(
(normalized_strain[:, :, None], delta[:, :, None], repeated), axis=2
)
stress_scale = float(np.std(stress[train]))
target = stress / stress_scale
return {
"samples": samples,
"pointwise": pointwise.astype(np.float32),
"history": history.astype(np.float32),
"target": target.astype(np.float32),
"stress_pa": stress,
"splits": splits,
"normalization": {
"parameter_names": list(PARAMETER_NAMES),
"parameter_mean": parameter_mean.tolist(),
"parameter_scale": parameter_scale.tolist(),
"strain_scale": strain_scale,
"stress_scale_pa": stress_scale,
},
}
def _train_pointwise(
features: np.ndarray,
target: np.ndarray,
train: np.ndarray,
validation: np.ndarray,
*,
epochs: int,
) -> PointwiseMLP:
model = PointwiseMLP(features.shape[-1])
optimizer = torch.optim.Adam(model.parameters(), lr=2.0e-3)
criterion = nn.MSELoss()
x_train = torch.from_numpy(features[train].reshape(-1, features.shape[-1]))
y_train = torch.from_numpy(target[train].reshape(-1, 1))
loader = DataLoader(TensorDataset(x_train, y_train), batch_size=4096, shuffle=True)
x_validation = torch.from_numpy(
features[validation].reshape(-1, features.shape[-1])
)
y_validation = torch.from_numpy(target[validation].reshape(-1, 1))
best: dict[str, torch.Tensor] | None = None
best_loss = float("inf")
remaining = 6
for _ in range(epochs):
model.train()
for x_batch, y_batch in loader:
optimizer.zero_grad()
loss = criterion(model(x_batch), y_batch)
loss.backward()
optimizer.step()
model.eval()
with torch.no_grad():
loss = float(criterion(model(x_validation), y_validation))
if loss < best_loss - 1.0e-6:
best_loss = loss
best = {name: value.detach().clone() for name, value in model.state_dict().items()}
remaining = 6
else:
remaining -= 1
if remaining == 0:
break
if best is not None:
model.load_state_dict(best)
return model
def _train_gru(
features: np.ndarray,
target: np.ndarray,
train: np.ndarray,
validation: np.ndarray,
*,
epochs: int,
) -> HistoryGRU:
model = HistoryGRU(features.shape[-1])
optimizer = torch.optim.Adam(model.parameters(), lr=2.0e-3)
criterion = nn.MSELoss()
loader = DataLoader(
TensorDataset(torch.from_numpy(features[train]), torch.from_numpy(target[train, :, None])),
batch_size=32,
shuffle=True,
)
x_validation = torch.from_numpy(features[validation])
y_validation = torch.from_numpy(target[validation, :, None])
best: dict[str, torch.Tensor] | None = None
best_loss = float("inf")
remaining = 8
for _ in range(epochs):
model.train()
for x_batch, y_batch in loader:
optimizer.zero_grad()
loss = criterion(model(x_batch), y_batch)
loss.backward()
optimizer.step()
model.eval()
with torch.no_grad():
loss = float(criterion(model(x_validation), y_validation))
if loss < best_loss - 1.0e-6:
best_loss = loss
best = {name: value.detach().clone() for name, value in model.state_dict().items()}
remaining = 8
else:
remaining -= 1
if remaining == 0:
break
if best is not None:
model.load_state_dict(best)
return model
def _predict(model: nn.Module, features: np.ndarray, *, batch_size: int) -> np.ndarray:
model.eval()
chunks: list[np.ndarray] = []
with torch.no_grad():
for start in range(0, len(features), batch_size):
chunks.append(model(torch.from_numpy(features[start : start + batch_size])).numpy())
return np.concatenate(chunks, axis=0).squeeze(-1)
def _metrics(reference: np.ndarray, prediction: np.ndarray) -> dict[str, float]:
error = prediction - reference
rmse = float(np.sqrt(np.mean(error**2)))
mae = float(np.mean(np.abs(error)))
centered = reference - float(np.mean(reference))
r2 = 1.0 - float(np.sum(error**2) / np.sum(centered**2))
scale = float(np.max(reference) - np.min(reference))
return {
"rmse_mpa": rmse / 1.0e6,
"mae_mpa": mae / 1.0e6,
"range_normalized_rmse": rmse / scale,
"r2": r2,
}
def train(*, mlp_epochs: int = 40, gru_epochs: int = 60) -> dict[str, object]:
torch.manual_seed(20260925)
np.random.seed(20260925)
torch.set_num_threads(min(4, torch.get_num_threads()))
torch.use_deterministic_algorithms(True)
arrays = _load_arrays()
splits = arrays["splits"]
train_mask = splits == "train"
validation_mask = splits == "validation"
test_mask = splits == "test"
started = time.perf_counter()
mlp = _train_pointwise(
arrays["pointwise"],
arrays["target"],
train_mask,
validation_mask,
epochs=mlp_epochs,
)
gru = _train_gru(
arrays["history"],
arrays["target"],
train_mask,
validation_mask,
epochs=gru_epochs,
)
stress_scale = arrays["normalization"]["stress_scale_pa"]
mlp_prediction = _predict(mlp, arrays["pointwise"], batch_size=128) * stress_scale
gru_prediction = _predict(gru, arrays["history"], batch_size=64) * stress_scale
reference = arrays["stress_pa"]
metrics: dict[str, object] = {
"status": "completed",
"task": "signed-equivalent-stress history prediction",
"split_policy": "complete trajectories; normalization fitted on train only",
"test_trajectory_count": int(np.count_nonzero(test_mask)),
"pointwise_mlp": {"overall": _metrics(reference[test_mask], mlp_prediction[test_mask])},
"history_gru": {"overall": _metrics(reference[test_mask], gru_prediction[test_mask])},
"wall_seconds": float(time.perf_counter() - started),
"torch_version": torch.__version__,
"normalization": arrays["normalization"],
}
samples = arrays["samples"]
for model_name in MODEL_NAMES:
mask = test_mask & np.asarray(
[sample["material_model"] == model_name for sample in samples]
)
metrics["pointwise_mlp"][model_name] = _metrics(reference[mask], mlp_prediction[mask])
metrics["history_gru"][model_name] = _metrics(reference[mask], gru_prediction[mask])
ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
(ARTIFACT_DIR / "baseline_metrics.json").write_text(
json.dumps(metrics, indent=2, sort_keys=True) + "\n", encoding="utf-8"
)
torch.save(
{
"pointwise_mlp": mlp.state_dict(),
"history_gru": gru.state_dict(),
"normalization": arrays["normalization"],
"parameter_names": PARAMETER_NAMES,
"model_names": MODEL_NAMES,
},
ARTIFACT_DIR / "baseline_models.pt",
)
fig, axes = plt.subplots(2, 3, figsize=(12.0, 7.2), constrained_layout=True)
for axis, family in zip(axes.flat, PATH_FAMILIES, strict=True):
index = next(
idx
for idx, sample in enumerate(samples)
if test_mask[idx]
and sample["material_model"] == "chaboche_combined"
and sample["path_family"] == family
)
strain = 100.0 * samples[index]["history"]["signed_equivalent_strain"]
axis.plot(strain, reference[index] / 1.0e6, color="#111827", lw=2.0, label="AgentFEM")
axis.plot(strain, mlp_prediction[index] / 1.0e6, color="#f59e0b", lw=1.2, label="Pointwise MLP")
axis.plot(strain, gru_prediction[index] / 1.0e6, color="#2563eb", lw=1.5, label="History GRU")
axis.set_title(family.replace("_", " ").title(), fontsize=10)
axis.set_xlabel("Signed equivalent strain (%)")
axis.set_ylabel("Signed equivalent stress (MPa)")
axis.grid(alpha=0.22)
axes.flat[0].legend(frameon=False, fontsize=8)
fig.suptitle("T2 v1 baseline comparison on held-out Chaboche trajectories", fontsize=13)
fig.savefig(ARTIFACT_DIR / "baseline_predictions.png", dpi=180)
plt.close(fig)
print(json.dumps(metrics, indent=2, sort_keys=True))
return metrics
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--mlp-epochs", type=int, default=40)
parser.add_argument("--gru-epochs", type=int, default=60)
args = parser.parse_args()
train(mlp_epochs=args.mlp_epochs, gru_epochs=args.gru_epochs)
if __name__ == "__main__":
main()
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