File size: 2,979 Bytes
f1564a0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | # 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.
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