Convolutional Differential Operators with PyTorch
Project description
ConvDO
Convolutional Differential Operators for Physics-based Deep Learning Study
Calculate the spatial derivative differentiablly!
Installation
- Install through pip:
pip install git+https://github.com/qiauil/ConvDO - Install locally: Download the repository and run
./install.shorpip install .
Feature
Positive😀 and negative🙃 things are all features...
- PyTorch-based and only supports 2D fields at the moment.
- Powered by convolutional neural network.
- Differentiable and GPU supported (why not? It's PyTorch based!).
- Second order for Dirichlet and Neumann boundary condition.
- Up to 8th order for periodic boundary condition.
- Obstacles inside of the domain is supported.
Documentations
Check 👉 here
Further Reading
Projects using ConvDO:
- Diffusion-based-Flow-Prediction: Diffusion-based flow prediction (DBFP) with uncertainty for airfoils.
- To be updated...
If you need to solve more complex PDEs using differentiable functions, please have a check on
- PhiFlow: A differentiable PDE solving framework for machine learning
- Exponax: Efficient Differentiable n-d PDE solvers in JAX.
For more research on physics based deep learning research, please visit the website of our research group at TUM.
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