Metadata-Version: 2.4
Name: jax-lab
Version: 0.6.0
Summary: A JAX-based differentiable Lattice Boltzmann library for single and multiphase flow simulations on CPUs, GPUs and TPUs
Author-email: Piyush Pradhan <pradhanpiyush687@gmail.com>
License-Expression: Apache-2.0
Project-URL: Repository, https://github.com/piyush-ppradhan/JAX-LaB
Project-URL: Paper, https://doi.org/10.1029/2025MS005313
Keywords: lattice-boltzmann,jax,cfd,multiphase,differentiable,gpu
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: jax==0.10.0
Requires-Dist: numpy
Requires-Dist: orbax-checkpoint
Provides-Extra: io
Requires-Dist: pyvista; extra == "io"
Requires-Dist: matplotlib; extra == "io"
Requires-Dist: Rtree; extra == "io"
Requires-Dist: trimesh; extra == "io"
Requires-Dist: h5py; extra == "io"
Provides-Extra: cuda13
Requires-Dist: jax[cuda13]==0.10.0; extra == "cuda13"
Provides-Extra: cuda12
Requires-Dist: jax[cuda12]==0.10.0; extra == "cuda12"
Provides-Extra: cuda13-local
Requires-Dist: jax[cuda13-local]==0.10.0; extra == "cuda13-local"
Provides-Extra: cuda12-local
Requires-Dist: jax[cuda12-local]==0.10.0; extra == "cuda12-local"
Provides-Extra: tpu
Requires-Dist: jax[tpu]==0.10.0; extra == "tpu"
Provides-Extra: rocm
Requires-Dist: jax[rocm7-local]==0.10.0; extra == "rocm"
Provides-Extra: dev
Requires-Dist: build; extra == "dev"
Requires-Dist: twine; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Requires-Dist: pytest; extra == "dev"
Provides-Extra: docs
Requires-Dist: zensical; extra == "docs"
Requires-Dist: mkdocstrings-python; extra == "docs"
Dynamic: license-file

<h1 align="center">JAX-LaB</h1>

<div align="center">
<h4>

[Documentation](https://piyush-ppradhan.github.io/JAX-LaB/)  |  [Paper](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025MS005313?af=R)
</h4>
</div>

A Python-based, differentiable, massively parallel lattice Boltzmann library for modeling multiphase and multiphysics flows & physics-based machine learning.

## Key Features
- **JAX Ecosystem Integration:** Works with machine learning libraries such as [Equinox](https://github.com/patrick-kidger/equinox), [Flax](https://github.com/google/flax), [Haiku](https://github.com/deepmind/dm-haiku), and [Optax](https://github.com/google-deepmind/optax).
- **Differentiable LBM:** Provides differentiable kernels for physics and deep learning applications.
- **Scalable and Portable:** Runs on multi-core CPUs, GPUs, and TPUs, with distributed support for simulations spanning hundreds of GPUs and billions of cells.
- **Broad LBM Support:** Includes several boundary conditions and collision kernels, along with Shan-Chen and volume-of-fluid (VoF) multiphase, multiphysics, and multicomponent flow modeling.
- **User-Friendly Python Interface:** Written entirely in Python, simplifying simulation setup and making library easy to extend.
- **JAX Array and Shardmap:** Offers a NumPy-like interface while leaving performance optimization to the compiler.
- **GPU-Optimized:** Per-shard boundary indices, scalar wetting/force stencils, and a fused symbolic MRT collision path reduce memory traffic and compiled kernel size.
- **Visualization:** Supports multiple output options, including JAX-native ray tracer for in situ surface, volume, and vector-field rendering of GPU/TPU arrays.

## Capabilities
### Multiphase Flow Modeling
### 1. Shan-Chen
- Support for **high density ratio flows** (tested for density ratios > 10<sup>10</sup>) using 4th-order isotropic explicit finite difference (EFM) method for gradient computation.
- Incorporates **Equation of State (EOS)** to model multiphase flows. Currently implemented EOS include **Carnahan-Starling**, **Peng-Robinson**, **Redlich-Kwong**, **Redlich-Kwong-Soave**
and **VanderWaals**.
- **Density ratio independent surface tension** control by directly modifying pressure tensor (MRT model).

### Multicomponent Flow Support
Computations use *pytrees* to **model any number of components**, each with its own equation of state, initial condition, and boundary conditions, without requiring library modifications.

### Wetting model
- [Geometric wetting scheme](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.87.013301)
- [Improved virtual density scheme](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.100.053313)

### 2. Thermal Flow
- **Hybrid thermal LBM solver** for two- and three-dimensional **single-phase, multiphase, and multicomponent** flows.
- Thermal equation is solved using lattice-based finite-difference stencils and **fourth-order Runge-Kutta** time integration.

### 3. Volume of Fluid (VoF)
- **Two-lattice free-surface LBM** (Mohammadrezaei et al., 2026): liquid and gas each evolve on their own lattice and are coupled only at interface cells, enabling **physical density ratios** (e.g., air-water 1:1000) with a sharp interface.
- **Surface tension** from the interface curvature, **contact angles** at walls, and an optional **viscous normal-stress jump**.
- **BGK** and **MRT** collision on **D2Q9** and **D3Q19**; interface work runs only on a compacted band of interface cells, so its cost scales with interface size.

### Collision Models
- **BGK**
- **Multi-Relaxation Time (MRT)**
- **Cascaded (Central Moment)**
- **KBC**

### Lattice
- D2Q9
- D3Q19
- D3Q27

### Machine Learning
- Easy integration with JAX's ecosystem of machine learning libraries
- Differentiable LBM kernels both for single and multiphase flows
- Differentiable boundary conditions

### Compute Capabilities
- Distributed Multi-GPU support
- Mixed-Precision support (store vs compute)
- Local, per-shard boundary condition indices instead of global lists replicated on every device
- Scalar neighbor stencils for wetting and Shan-Chen force, avoiding per-direction streamed arrays
- Fused, symbolic MRT collision matrix

### Output
- Binary and ASCII VTK output using [PyVista](https://docs.pyvista.org/)
- HDF5/XDMF output using [h5py](https://docs.h5py.org/)
- JAX-native in-situ surface, refractive volume, and vector-field rendering and image output
- Distributed asynchronous checkpointing using [orbax](https://github.com/google/orbax) 
- 3D mesh voxelizer using [trimesh](https://trimesh.org/)

### Boundary Conditions
- **Equilibrium:** Sets prescribed velocity or pressure using equilibrium populations.
- **Full-Way Bounceback:** Reflects populations to impose a stationary, no-slip wall.
- **Half-Way Bounceback:** Imposes a no-slip wall halfway between fluid and solid nodes.
- **Do Nothing:** Allows populations to pass through unmodified.
- **Zou-He:** Imposes a prescribed velocity or pressure profile.
- **Regularized:** Provides a more stable, but more expensive, alternative to Zou-He.
- **Extrapolation Outflow:** Reduces wave reflections using extrapolation.
- **Non-Equilibrium Extrapolation:** Open boundary condition with prescribed density.
- **Exact Non-Equilibrium Extrapolation:** Mass-corrected open boundary condition with prescribed density.
- **Interpolated Bounceback:** Applies the Bouzidi scheme to curved or off-lattice walls.
- **Convective Outflow:** Supports outflow in applications such as porous media flow.
- **Dirichlet:** Prescribes temperature at the boundary.
- **Neumann:** Prescribes the normal temperature gradient.

## Documentation
Complete API documentation is available [here](https://piyush-ppradhan.github.io/JAX-LaB/), or you can build and preview it locally:
```bash
pip install -e ".[docs]"
zensical serve
```

## Installation Guide
JAX-LaB is distributed as the `jax-lab` package (import name `jax_lab`). The default install targets CPU:
```bash
pip install jax-lab
```

### Accelerator support
Hardware acceleration is selected through dependency extras, which delegate the compiled backend packages to [JAX's own extras](https://docs.jax.dev/en/latest/installation.html):
```bash
pip install "jax-lab[cuda13]"   # NVIDIA GPU (CUDA 13, bundled)
pip install "jax-lab[cuda12]"   # NVIDIA GPU (CUDA 12, bundled)
pip install "jax-lab[tpu]"      # Google TPU
pip install "jax-lab[rocm]"     # AMD GPU (ROCm, local toolkit)
```
Use `cuda13-local`/`cuda12-local` instead if you manage the CUDA toolkit yourself.

### Optional I/O and visualization dependencies
The I/O and visualization utilities load their dependencies lazily (at call time, not at import time), so the core solver runs without them. The following packages are only needed if you call the corresponding functions:

| Package | Required by |
|---|---|
| [PyVista](https://docs.pyvista.org/) | `save_fields_vtk`, `save_BCs_vtk`, `live_volume_rendering` |
| [h5py](https://docs.h5py.org/) | `save_fields_hdf5_xdmf` |
| [matplotlib](https://matplotlib.org/) | `save_image`, `live_volume_rendering` |
| [trimesh](https://trimesh.org/) + [Rtree](https://rtree.readthedocs.io/en/stable/) | `voxelize_stl` |

Calling one of these functions without its dependency installed raises an `ImportError` naming the missing package. The `io` extra installs all of them at once (recommended for running the examples, most of which write VTK or image output):
```bash
pip install "jax-lab[io]"
```
Extras can be combined, e.g. `pip install "jax-lab[cuda13,io]"`.

### Development install
To work on JAX-LaB itself or run the bundled examples, install from source in editable mode:
```bash
git clone https://github.com/piyush-ppradhan/JAX-LaB
cd JAX-LaB
pip install -e ".[dev,io]"
```

> [!NOTE]
> On macOS, please use the standard CPU installation, as JAX does not support GPU acceleration on this platform.

Run an example:
```bash
python3 examples/isothermal/singlephase/cavity2d.py
```
## Citation
```bibtex
@article{pradhan_jax-lab_2026,
    title = {{JAX}-{LaB}: {A} {High}-{Performance}, {Differentiable} {Lattice} {Boltzmann} {Library} for {Modeling} {Multiphase} {Fluid} {Dynamics} in {Geosciences} and {Engineering}},
    volume = {18},
    copyright = {© 2026 The Author(s). Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union.},
    issn = {1942-2466},
    shorttitle = {{JAX}-{LaB}},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2025MS005313},
    doi = {10.1029/2025MS005313},
    language = {en},
    number = {2},
    urldate = {2026-02-20},
    journal = {Journal of Advances in Modeling Earth Systems},
    author = {Pradhan, Piyush and Gentine, Pierre and Kelly, Shaina},
    year = {2026},
    keywords = {GPU, JAX, Lattice Boltzmann method, Python, Shan-Chen method, multiphase flow},
    pages = {e2025MS005313},
}
```
