XLB: A differentiable massively parallel lattice Boltzmann library in Python

• URL: http://arxiv.org/abs/2311.16080v3
• Date: Tue, 2 Apr 2024 15:56:38 GMT
• Title: XLB: A differentiable massively parallel lattice Boltzmann library in Python
• Authors: Mohammadmehdi Ataei, Hesam Salehipour,
• Abstract summary: We introduce XLB library, a Python-based differentiable LBM library based on the JAX platform. XLB's differentiability and data structure is compatible with the extensive JAX-based machine learning ecosystem. XLB has been successfully scaled to handle simulations with billions of cells, achieving giga-scale lattice updates per second.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The lattice Boltzmann method (LBM) has emerged as a prominent technique for solving fluid dynamics problems due to its algorithmic potential for computational scalability. We introduce XLB library, a Python-based differentiable LBM library based on the JAX platform. The architecture of XLB is predicated upon ensuring accessibility, extensibility, and computational performance, enabling scaling effectively across CPU, TPU, multi-GPU, and distributed multi-GPU or TPU systems. The library can be readily augmented with novel boundary conditions, collision models, or multi-physics simulation capabilities. XLB's differentiability and data structure is compatible with the extensive JAX-based machine learning ecosystem, enabling it to address physics-based machine learning, optimization, and inverse problems. XLB has been successfully scaled to handle simulations with billions of cells, achieving giga-scale lattice updates per second. XLB is released under the permissive Apache-2.0 license and is available on GitHub at https://github.com/Autodesk/XLB.

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