| # Start here: AgentFEM Material Loading Memory v1 |
|
|
| This repository is a research handoff, not only a file archive. It contains the |
| published dataset, the exact generation configuration, reusable readers, |
| reference baselines, numerical tests, quality evidence and a concrete route for |
| extending the work into a research article. |
|
|
| ## Choose your route |
|
|
| ### 1. Use the data or train a new model |
|
|
| You do **not** need AgentFEM. Create the lightweight environment and verify the |
| download: |
|
|
| ```bash |
| conda env create -f environment-use.yml |
| conda activate agentfem-t2-use |
| bash reproduce_t2_v1.sh verify |
| ``` |
|
|
| Start with `src/load_t2_material_loading_memory.py`. The current HDF5 data can |
| be used directly for GRU, LSTM, transformer, state-space or operator-learning |
| experiments. |
|
|
| ### 2. Reproduce the published baseline |
|
|
| ```bash |
| bash reproduce_t2_v1.sh baseline |
| ``` |
|
|
| This verifies the data and retrains the pointwise MLP and history-aware GRU. |
| The published reference metrics are in |
| `artifacts/t2_material_loading_memory_v1/baseline_metrics.json`. |
|
|
| ### 3. Regenerate the physics data |
|
|
| Use `environment-reproduce.yml`, which installs FEniCSx and the exact AgentFEM |
| commit used for v1: |
|
|
| ```bash |
| conda env create -f environment-reproduce.yml |
| conda activate agentfem-t2-reproduce |
| bash reproduce_t2_v1.sh design |
| bash reproduce_t2_v1.sh full |
| ``` |
|
|
| Full regeneration is deterministic but rewrites generated shards and baseline |
| artifacts in the working copy. See `REPRODUCE.md` before running it. |
|
|
| ## What is already established |
|
|
| - 1,008 complete trajectories, not 121,968 independent frames. |
| - Two explicitly labelled constitutive models: J2 linear isotropic hardening |
| and Chaboche combined hardening. |
| - Six balanced proportional loading-path families. |
| - Complete-trajectory train/validation/test splits with no frame leakage. |
| - Numerical consistency, analytical, refinement and checksum evidence. |
| - A strong history-aware baseline and a deliberately history-blind control. |
|
|
| ## What remains for a strong paper |
|
|
| The current release is a verified foundation, not the full paper claim. The |
| recommended first paper studies generalization of learned path-dependent |
| constitutive models. Its priority additions are true non-proportional |
| multiaxial paths, physically meaningful out-of-distribution splits, |
| physics-aware metrics, stronger sequence-model comparisons and deployment in a |
| small structural finite-element example. See `PAPER_ROADMAP.md`. |
|
|
| ## Source of truth and citation |
|
|
| - Dataset: <https://huggingface.co/datasets/HaomingLuo/AgentFEM-Material-Loading-Memory> |
| - AgentFEM: <https://github.com/haoming-luo/agentfem> |
| - AgentFEM commit used for v1: |
| `058faecc05aeda143d014fd229401003a9258bbb` |
| - Dataset license: CC BY 4.0 |
| - Included source-code license: Apache-2.0 |
|
|
| When extending the dataset, preserve trajectory-level grouping, units, model |
| labels, random seeds, quality failures and the original v1 test set. Record a |
| new schema or dataset version whenever fields or physical scope change. |
|
|
