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It's PyTorch based!).\n* Second order for Dirichlet and Neumann boundary condition.\n* Up to 8th order for periodic boundary condition.\n* Obstacles inside of the domain is supported.\n\n## Documentations\n\nCheck 👉 [here](https://qiauil.github.io/ConvDO/)\n\n## Further Reading\n\nProjects using `ConvDO`:\n\n* [Diffusion-based-Flow-Prediction](https://github.com/tum-pbs/Diffusion-based-Flow-Prediction): Diffusion-based flow prediction (DBFP) with uncertainty for airfoils.\n* To be updated... \n\nIf you need to solve more complex PDEs using differentiable functions, please have a check on\n\n* [PhiFlow](https://github.com/tum-pbs/PhiFlow): A differentiable PDE solving framework for machine learning\n* [Exponax](https://github.com/Ceyron/exponax): Efficient Differentiable n-d PDE solvers in JAX.\n\nFor more research on physics based deep learning research, please visit the website of [our research group at TUM](https://ge.in.tum.de/publications/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqiauil%2Fconvdo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqiauil%2Fconvdo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqiauil%2Fconvdo/lists"}