File size: 1,887 Bytes
1456993 | 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 | """Minimal offline inference for the material-conditioned DENIM v2 bundle."""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
import torch
from safetensors.torch import load_file
def run(bundle: Path) -> dict[str, float]:
repository_root = bundle.parent
if (repository_root / "src").is_dir():
sys.path.insert(0, str(repository_root))
from src.t2_denim_conditional import ConditionalDENIM, material_descriptor, rollout
model = ConditionalDENIM()
model.load_state_dict(load_file(bundle / "weights.safetensors"))
model.eval()
parameters = {
"material_model": "chaboche_combined",
"young_pa": 190.0e9,
"poisson": 0.30,
"yield_stress_pa": 280.0e6,
"backstress_c1_pa": 28.0e9,
"backstress_gamma1": 80.0,
"backstress_c2_pa": 10.0e9,
"backstress_gamma2": 12.0,
"isotropic_saturation_pa": 70.0e6,
"isotropic_rate": 8.0,
}
strain = torch.zeros((1, 121, 6))
strain[0, :, 0] = torch.linspace(0.0, 0.01, 121)
with torch.no_grad():
result = rollout(
strain,
torch.tensor([parameters["young_pa"]]),
torch.tensor([parameters["poisson"]]),
torch.tensor([parameters["yield_stress_pa"]]),
material_descriptor(parameters)[None],
model,
)
summary = {
"maximum_absolute_stress_mpa": float(result["stress"].abs().max() / 1.0e6),
"final_peeq": float(result["peeq"][0, -1]),
"maximum_yield_residual_pa": float(result["yield_residual"].abs().max()),
}
print(json.dumps(summary, indent=2))
return summary
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--bundle", type=Path, default=Path(__file__).resolve().parent)
run(parser.parse_args().bundle)
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