Physics-informed neural network parts for ___. Codes are based on https://github.com/neuraloperator/physics_informed (Z. Li, et. al. 2022.) and https://github.com/BaratiLab/Diffusion-based-Fluid-Super-resolution (D. Shu, et. al. 2023) with some modifications for further research.
conda create -n pinn4 python=3.9
conda activate pinn4
pip install --upgrade pip
pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2
conda install -c conda-forge deepxde
pip install pyDOE
NS_generator.ipynb (above figure can be obtained with changing args.Re=100, 1000, 10000 in the second code block)
- spatial domain:
$x\in (0, 2\pi)^2$ - temporal domain:
$t \in [0, 0.5]$ - forcing:
$-4\cos(4x_2)$ - Reynolds number: 500
Data of shape (N, T, X, Y) where N is the number of instances, T is temporal resolution, X, Y are spatial resolutions.
- NS_Re500_s256_T100_test.npy: 100x129x256x256
- spatial domain:
$x\in (0, 2\pi)^2$ - temporal domain:
$t \in [0, 10]$ - forcing:
$-4\cos(4x_2) -0.1\omega(x, t)$ - Reynolds number: 1000
Data of shape (N, T, X, Y) where N is the number of instances, T is temporal resolution, X, Y are spatial resolutions.
- (kf_2d_re1000_256_40seed): 40x320x256x256
- PINN loss for Navier Stokes
- Fourier neural operator with pinn loss in spectral space.
python train_pdeloss.py --tqdm