Added the white paper

readme.md

@@ -217,6 +217,8 @@ 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: Distributed Multi-GPU Lattice Boltzmann Simulation Framework for Differentiable Scientific Machine Learning__. Mohammadmehdi Ataei, Hesam Salehipour. _arXiv 2023_.](https://arxiv.org/abs/2311.16080) - White paper describing the [XLB](https://github.com/Autodesk/XLB) library: benchmarks, validations, and more details about the library.
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 <!--lint enable awesome-list-item-->
 
 <a name="tutorials-and-blog-posts" />