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The lattice Boltzmann method (LBM) has emerged as a prominent technique forsolving fluid dynamics problems due to its algorithmic potential forcomputational scalability. We introduce XLB library, a Python-baseddifferentiable LBM library based on the JAX platform. The architecture of XLBis predicated upon ensuring accessibility, extensibility, and computationalperformance, enabling scaling effectively across CPU, TPU, multi-GPU, anddistributed multi-GPU or TPU systems. The library can be readily augmented withnovel boundary conditions, collision models, or multi-physics simulationcapabilities. XLB's differentiability and data structure is compatible with theextensive JAX-based machine learning ecosystem, enabling it to addressphysics-based machine learning, optimization, and inverse problems. XLB hasbeen successfully scaled to handle simulations with billions of cells,achieving giga-scale lattice updates per second. XLB is released under thepermissive Apache-2.0 license and is available on GitHub athttps://github.com/Autodesk/XLB.

XLB: A Differentiable Massively Parallel Lattice Boltzmann Library in Python