T2_V2_START_HERE.md

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.