JAX-Fluids is a fully-differentiable CFD solver for 3D, compressible single-phase and two-phase flows. We developed this package with the intention to facilitate research at the intersection of ML and CFD. It is easy to use - running a simulation only requires a couple lines of code. Written entirely in JAX, the solver runs on CPU/GPU/TPU and enables automatic differentiation for end-to-end optimization of numerical models. JAX-Fluids is parallelized using JAX primitives and scales efficiently on state-of-the-art HPC clusters (tested on up to 512 NVIDIA A100 GPUs and on up to 2048 TPU-v3 cores).

To learn more about implementation details and details on numerical methods provided by JAX-Fluids, feel free to read our papers here and here. And also check out the documentation of JAX-Fluids.

Authors:

• Deniz A. Bezgin
• Aaron B. Buhendwa
• Nikolaus A. Adams

Correspondence via mail.

JAX-Fluids solves the Navier-Stokes-equations using the finite-volume-method on a Cartesian grid. The current version provides the following features:

• Explicit time stepping (Euler, RK2, RK3)
• High-order adaptive spatial reconstruction (WENO-3/5/7, WENO-CU6, WENO-3NN, TENO)
• Riemann solvers (Lax-Friedrichs, Rusanov, HLL, HLLC, Roe)
• Implicit turbulence sub-grid scale model ALDM
• Two-phase simulations via level-set method and diffuse-interface method
• Immersed solid boundaries via level-set method
• Positivity-preserving techniques
• Forcings for temperature, mass flow rate and kinetic energy spectrum
• Boundary conditions: Symmetry, Periodic, Wall, Dirichlet, Neumann
• CPU/GPU/TPU capability
• Parallel simulations on GPU & TPU

Space shuttle at Mach 2 - Immersed solid boundary method via level-set

Shock-bubble interaction with diffuse-interface method - approx. 800M cells on TPUv3-64

Shock-bubble interaction with level-set method - approx. 2B cells on TPUv3-256

Shock-induced collapse of air bubbles in water (click link for video)

https://www.youtube.com/watch?v=mt8HjZhm60U

Before installing JAX-Fluids, please ensure that you have an up-to-date version of pip.

pip install --upgrade pip

To install the CPU-only version of JAX-Fluids, you can run

pip install --upgrade "jax[cpu]"
git clone https://github.com/tumaer/JAXFLUIDS.git
cd JAXFLUIDS
pip install .

Note: if you want to install JAX-Fluids in editable mode, e.g., for code development on your local machine, run

pip install -e .

Note: if you want to use jaxlib on a Mac with M1 chip, check the discussion here.

If you want to install JAX-Fluids with CPU AND GPU support, you must first install JAX with GPU support. There are two ways to do this:

1. installing CUDA & cuDNN via pip,
2. installing CUDA & cuDNN by yourself.

See JAX installation for details.

We recommend installing CUDA & cuDNN using pip wheels:

pip install --upgrade "jax[cuda12]"
git clone https://github.com/tumaer/JAXFLUIDS.git
cd JAXFLUIDS
pip install -e .

For more information on JAX on GPU please refer to the github of JAX

This github contains five jupyter-notebooks which will get you started quickly. They demonstrate how to run simple simulations like a 1D sod shock tube or a 2D air-helium shock-bubble interaction. Furthermore, they show how you can easily switch the numerical and/or case setup in order to, e.g., increase the order of the spatial reconstruction stencil or decrease the resolution of the simulation.

Check out the documentation of JAX-Fluids.

We gratefully acknowledge access to TPU compute resources granted by Google's TRC program.

JAX-Fluids 2.0: Towards HPC for Differentiable CFD of Compressible Two-phase Flows https://arxiv.org/abs/2402.05193

@article{bezgin2024jax,
  title={JAX-Fluids 2.0: Towards HPC for Differentiable CFD of Compressible Two-phase Flows},
  author={Bezgin, Deniz A and Buhendwa, Aaron B and Adams, Nikolaus A},
  journal={arXiv preprint arXiv:2402.05193},
  year={2024}
}

JAX-Fluids: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows https://doi.org/10.1016/j.cpc.2022.108527

@article{BEZGIN2022108527,
   title = {JAX-Fluids: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows},
   journal = {Computer Physics Communications},
   pages = {108527},
   year = {2022},
   issn = {0010-4655},
   doi = {https://doi.org/10.1016/j.cpc.2022.108527},
   url = {https://www.sciencedirect.com/science/article/pii/S0010465522002466},
   author = {Deniz A. Bezgin and Aaron B. Buhendwa and Nikolaus A. Adams},
   keywords = {Computational fluid dynamics, Machine learning, Differential programming, Navier-Stokes equations, Level-set, Turbulence, Two-phase flows}
} 

This project is licensed under the GNU General Public License v3 - see the LICENSE file or for details https://www.gnu.org/licenses/.