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This is a native PyTorch port of Google's Computational Fluid Dynamics package in Jax. The main changes are documented in the `README.md` under the [`torch_cfd` directory](torch_cfd/README.md). The biggest change is many routines that rely on the functional programming of Jax have been rewritten to be a more PyTorch-friendly tensor-in to tensor-out style.
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This is a native PyTorch port of [Google's Computational Fluid Dynamics package in Jax](https://github.com/google/jax-cfd). The main changes are documented in the `README.md` under the [`torch_cfd` directory](torch_cfd/README.md). The biggest change is many routines that rely on the functional programming of Jax have been rewritten to be a more PyTorch-friendly tensor-in to tensor-out style.
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## Installation
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```bash
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pip install torch-cfd
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```
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## Contributions
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PR welcome. Current the port only includes:
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- Pseudospectral methods for vorticity which use anti-aliasing filtering techniques for non-linear terms to maintain stability.
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- Temporal discretization: Currently only RK4 temporal discretization, using explicit time-stepping for advection and either implicit or explicit time-stepping for diffusion.
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- Boundary conditions: only periodic boundary conditions.
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## Examples
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- Demos of different simulation setups:
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-[2D simulation with a psuedo-spectral solver](example_Kolmogrov2d_rk4_cn_forced_turbulence.ipynb)
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-[2D simulation with a psuedo-spectral solver](example_Kolmogrov2d_rk4_cn_forced_turbulence.ipynb)
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