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Add clarifications to the README
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shoyer authored and JAX-CFD authors committed May 17, 2021
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Authors: Dmitrii Kochkov, Jamie A. Smith, Stephan Hoyer

JAX-CFD is a research project for exploring the potential of machine learning,
automatic differentiation and hardware accelerators (GPU/TPU) for computational
fluid dynamics. It is implemented in [JAX](https://github.com/google/jax).
JAX-CFD is an experimental research project for exploring the potential of
machine learning, automatic differentiation and hardware accelerators (GPU/TPU)
for computational fluid dynamics. It is implemented in
[JAX](https://github.com/google/jax).

To learn more about our general approach, read our paper [Machine learning accelerated computational fluid dynamics](https://arxiv.org/abs/2102.01010).

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Take a look at "demo" notebook in the "notebooks" directory for an example of
how to use JAX-CFD for simulate a 2D turbulent flow.

TODO: add an example notebook using our pre-trained turbulence models.
We are currently preparing more example notebooks, inculding:

- Reusing our training data and/or evaluation setup (without running JAX-CFD)
- Simulations using our pre-trained turbulence models.
- Training a simple hybrid ML + CFD model from scratch

## Organization

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