2026-10-15T10:19:31Z https://data.mendeley.com/oai

oai:data.mendeley.com/n25h65vw59.1 2024-04-16T12:57:49Z

Ballantyne, John XLB: A differentiable massively parallel lattice Boltzmann library in Python Mendeley Data 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. Computational Physics Machine Learning Lattice-Boltzmann Method Ataei, Mohammadmehdi Salehipour, Hesam Dataset doi:10.17632/n25h65vw59.1 oai:data.mendeley.com/n25h65vw59.1 Apache License 2.0 http://www.apache.org/licenses/LICENSE-2.0 https://data.mendeley.com/datasets/n25h65vw59 2024-04-16T12:57:49Z