| """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) |
|
|
