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Added XLB library #94

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merged 6 commits into from
Dec 25, 2023
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Added XLB library #94

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ddc8fc1
Added XLB library
mehdiataei Nov 28, 2023
133a46b
Added the white paper
mehdiataei Nov 28, 2023
c59a2a4
Update readme.md
mehdiataei Dec 24, 2023
c6b68a2
Add lint double link ignore
n2cholas Dec 24, 2023
2ef34ee
Add ignore double link
n2cholas Dec 24, 2023
0f0476e
Fixed the linter issue
mehdiataei Dec 24, 2023
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5 changes: 5 additions & 0 deletions readme.md
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Expand Up @@ -112,6 +112,9 @@ This section contains libraries that are well-made and useful, but have not nece
- [QDax](https://github.com/adaptive-intelligent-robotics/QDax) - Quality Diversity optimization in Jax. <img src="https://img.shields.io/github/stars/adaptive-intelligent-robotics/QDax?style=social" align="center">
- [JAX Toolbox](https://github.com/NVIDIA/JAX-Toolbox) - Nightly CI and optimized examples for JAX on NVIDIA GPUs using libraries such as T5x, Paxml, and Transformer Engine. <img src="https://img.shields.io/github/stars/NVIDIA/JAX-Toolbox?style=social" align="center">
- [Pgx](http://github.com/sotetsuk/pgx) - Vectorized board game environments for RL with an AlphaZero example. <img src="https://img.shields.io/github/stars/sotetsuk/pgx?style=social" align="center">
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- [XLB](https://github.com/Autodesk/XLB) - A Differentiable Massively Parallel Lattice Boltzmann Library in Python for Physics-Based Machine Learning. <img src="https://img.shields.io/github/stars/Autodesk/XLB?style=social" align="center">
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<a name="models-and-projects" />

Expand Down Expand Up @@ -216,6 +219,8 @@ This section contains papers focused on JAX (e.g. JAX-based library whitepapers,
- [__Compiling machine learning programs via high-level tracing__. Roy Frostig, Matthew James Johnson, Chris Leary. _MLSys 2018_.](https://mlsys.org/Conferences/doc/2018/146.pdf) - White paper describing an early version of JAX, detailing how computation is traced and compiled.
- [__JAX, M.D.: A Framework for Differentiable Physics__. Samuel S. Schoenholz, Ekin D. Cubuk. _NeurIPS 2020_.](https://arxiv.org/abs/1912.04232) - Introduces JAX, M.D., a differentiable physics library which includes simulation environments, interaction potentials, neural networks, and more.
- [__Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization__. Pranav Subramani, Nicholas Vadivelu, Gautam Kamath. _arXiv 2020_.](https://arxiv.org/abs/2010.09063) - Uses JAX's JIT and VMAP to achieve faster differentially private than existing libraries.
- [__XLB: A Differentiable Massively Parallel Lattice Boltzmann Library in Python__. Mohammadmehdi Ataei, Hesam Salehipour. _arXiv 2023_.](https://arxiv.org/abs/2311.16080) - White paper describing the XLB library: benchmarks, validations, and more details about the library.

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<a name="tutorials-and-blog-posts" />
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