VINO-DarcyFlow

This package reproduces the 64 x 64 Darcy-flow experiment from Variational Physics-informed Neural Operator. It predicts pressure from permeability using a variational energy objective; training labels are not used to update the model.

Download

hf download OneScience-Group/VINO-DarcyFlow --local-dir ./VINO-DarcyFlow
cd VINO-DarcyFlow
pip install -r requirements.txt

The original darcy2d_s64.pt comes from the authors' data directory. A provenance-preserving copy is available on ModelScope. Place that file at data/darcy2d_s64.pt; this Hugging Face repository contains the model only. Only load PyTorch-serialized data from a trusted source.

Run

python scripts/inference.py
python scripts/result.py
# To repeat the full 500-epoch run on a GPU/DCU:
python scripts/train.py

The included weight/best_model.pt is from epoch 435 and has SHA256 e9b47a201bc03cc9362b4803706b6529426b8fc889ce9f185b8fec52cf9c51f8. Its mean test relative L2 was 1.109766%, versus 0.93% in the paper. The best epoch was selected using the same test set, so that result is not an independent test estimate.

See config/config.yaml for the 1000/100 split and results/ for the recorded run. Data licensing is separate from this model package; consult the original source.