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| # Start here: T2 multiaxial OOD research release | |
| This package is designed for a collaborator or AI agent to continue the | |
| research without reconstructing the project history. | |
| ## Read in this order | |
| 1. `README.md` for the complete dataset overview. | |
| 2. `artifacts/t2_multiaxial_ood_v2/QUALITY_REPORT.md` for physics validation. | |
| 3. `artifacts/t2_multiaxial_ood_v2/model_metrics.json` for all model results. | |
| 4. `models/t2_multiaxial_ood_v2/README.md` before loading checkpoints. | |
| 5. `docs/T2_MULTIAXIAL_RESEARCH_MEMO.md` for the manuscript argument and next | |
| method contribution. | |
| 6. `T2_V2_REPRODUCE.md` for exact commands. | |
| ## What may be claimed now | |
| - The data generation and physical audits passed. | |
| - History-aware models strongly outperform a history-blind control on ID data. | |
| - Path-family OOD is substantially harder than the frozen parameter-OOD test. | |
| - Selected hard physical constraints remove corresponding state violations. | |
| - A state-output physics GRU did not transfer reliably to the structural gate. | |
| - A physics-embedded neural stress integrator reduced path-OOD RMSE to 0.0237 | |
| MPa and passed both frozen structural gates; the severe case recorded one | |
| trust-region fallback at complete unloading. | |
| ## What may not be claimed now | |
| - production-ready neural constitutive modeling; | |
| - general 3D AgentFEM learned-material integration; | |
| - experimental-material accuracy; | |
| - transfer of the white-box result to unknown or misspecified material laws; | |
| - general structural convergence without the recorded global fallback; | |
| - end-to-end acceleration in the current Python structural demonstrator. | |
| ## Highest-value continuation | |
| Develop a gray-box evolution model in which the embedded constitutive skeleton | |
| is deliberately incomplete and the network learns missing evolution terms or | |
| latent internal variables. Add a verified consistent tangent, 2D/3D gates and | |
| experimental or high-fidelity microstructural data. Retain the current splits | |
| and structural gates unchanged so improvement is measurable. | |