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@@ -113,6 +113,9 @@ 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">
+<!--lint ignore double-link-->
+- [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">
+<!--lint enable double-link-->
 
 <a name="models-and-projects" />
 
@@ -217,6 +220,8 @@ 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" />