| license: other | |
| language: | |
| - en | |
| tags: | |
| - OneScience | |
| - computational-fluid-dynamics | |
| - cylinder-wake | |
| - physics-informed-neural-network | |
| # TSA-PINN-CylinderWake | |
| This package reproduces the Re=100 cylinder-wake experiment of [Physics-informed neural networks with trainable sinusoidal activation functions](https://publications.polymtl.ca/65937/1/2025_Khademi_Physics-informed_neural_networks_trainable.pdf). A four-layer, 50-unit PINN predicts velocity and pressure from space-time coordinates. | |
| ## Download | |
| ```bash | |
| 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](https://github.com/AmirhosseinnnKhademi/TSA-PINN) and on [ModelScope](https://www.modelscope.cn/datasets/OneScience/TSA-PINN-CylinderWake). Place it at `data/cylinder_nektar_wake.mat`. No dataset is mirrored in this Hugging Face model repository. | |
| ## Run | |
| ```bash | |
| 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. | |