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:
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 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:
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.