REPRODUCE.md

Reproducing AgentFEM Material Loading Memory v1

Reproducibility levels

The release supports three distinct levels. Do not confuse them.

1. Data verification checks the eight HDF5 shard hashes, schema, trajectory IDs, splits, model labels, path-family counts and finite values. It requires only Python, NumPy and h5py.
2. Baseline reproduction retrains the published MLP and GRU from the frozen trajectory splits. CPU training is sufficient.
3. Physics regeneration recreates all trajectories with the exact AgentFEM source revision, repackages the shards and reruns the numerical audit.

Obtain the release

Using the Hugging Face CLI:

hf download HaomingLuo/AgentFEM-Material-Loading-Memory \
  --repo-type dataset \
  --local-dir AgentFEM-Material-Loading-Memory
cd AgentFEM-Material-Loading-Memory

Verify the downloaded data

conda env create -f environment-use.yml
conda activate agentfem-t2-use
bash reproduce_t2_v1.sh verify

Expected summary:

• 1,008 unique trajectories;
• 768/120/120 train/validation/test trajectories;
• 504 J2 and 504 Chaboche trajectories;
• 168 trajectories in each of six path families;
• eight shard hashes matching manifest.json.

Reproduce the baselines

bash reproduce_t2_v1.sh baseline

The script trains with the published defaults: 40 MLP epochs and 60 GRU epochs. Small numerical variation across PyTorch versions and hardware is normal. Compare with artifacts/t2_material_loading_memory_v1/baseline_metrics.json, not by requiring bitwise-identical neural-network weights.

Regenerate the physics data

The frozen physics environment uses Python 3.11, FEniCSx/DOLFINx 0.11.0 and AgentFEM commit 058faecc05aeda143d014fd229401003a9258bbb.

conda env create -f environment-reproduce.yml
conda activate agentfem-t2-reproduce
bash reproduce_t2_v1.sh design
bash reproduce_t2_v1.sh full

design checks the deterministic 1,008-case Sobol design without solving. full performs the following steps:

1. regenerate or resume all 1,008 material-point trajectories;
2. build eight HDF5 shards;
3. run analytical, constitutive and time-resolution audits;
4. retrain both reference baselines;
5. run the published automated tests.

The generator is restartable. For controlled parallel work, different workers may run non-overlapping ranges before one worker packages and audits:

python src/t2_material_loading_memory_v1.py --range 0 252
python src/t2_material_loading_memory_v1.py --range 252 504
python src/t2_material_loading_memory_v1.py --range 504 756
python src/t2_material_loading_memory_v1.py --range 756 1008
python src/t2_material_loading_memory_v1.py --package
python src/t2_material_loading_memory_v1.py --audit

Exact source fallback

If the VCS entry in environment-reproduce.yml cannot install AgentFEM, install it explicitly after creating the environment:

git clone https://github.com/haoming-luo/agentfem.git
cd agentfem
git checkout 058faecc05aeda143d014fd229401003a9258bbb
python -m pip install .
cd ..

Confirm before regeneration:

python -c "import agentfem; print(agentfem.__version__)"
git -C agentfem rev-parse HEAD

Reproducibility contract for extensions

• A sample is one complete trajectory; frames must not cross data splits.
• Keep SI units and record all material parameters and load-path controls.
• Preserve v1 as an immutable benchmark. Publish extensions under a new dataset version and document migration rules.
• Failed or non-converged cases must be retained in a failure ledger rather than silently removed.
• Split generation must be deterministic and grouped by trajectory and source design.
• Report both predictive errors and physical-consistency errors.