TSA-PINN-CylinderWake

This package reproduces the Re=100 cylinder-wake experiment of Physics-informed neural networks with trainable sinusoidal activation functions. A four-layer, 50-unit PINN predicts velocity and pressure from space-time coordinates.

Download

hf download OneScience-Group/TSA-PINN-CylinderWake --local-dir ./TSA-PINN-CylinderWake
cd TSA-PINN-CylinderWake
pip install -r requirements.txt

The original cylinder_nektar_wake.mat is available in the authors' repository and on ModelScope. Place it at data/cylinder_nektar_wake.mat. No dataset is mirrored in this Hugging Face model repository.

Run

python scripts/inference.py
python scripts/result.py
# To repeat the full 4000-update run on a GPU/DCU:
python scripts/train.py

The included weight/best_model.pt is from update 3956; its SHA256 is 5fadc91405b4741288750c775c09d043c205a625e630d1ee2a9e8b5022712e0f. At t=10, measured DNS relative L2 errors are 0.021981 for u and 0.083490 for v. The paper reports 0.01960 and 0.06630, but its aggregate evaluation convention is not fully specified, so the figures are not a strict like-for-like comparison.

See config/config.yaml and results/ for the experiment settings and outputs. The original data's usage terms should be checked separately.