mlsanityData to 14 Oct 2026

XLB: A differentiable massively parallel lattice Boltzmann library in Python

arXiv 2311.16080 ↗First posted 27 Nov 2023physics.comp-ph cs.CE cs.LGcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 19 of its 89 references that are in the map. First posted on arXiv 27 Nov 2023.

Builds on

19 of 89 references.

References · 19

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7 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 6; 2026: 2.

11 papers.

  1. 2024JAX-Fluids 2.0: Towards HPC for Differentiable CFD of Compressible Two-phase Flowscited by 7
  2. 2025Adaptive Physics-informed Neural Networks: A Surveycited by 5
  3. 2024Reduced-order modeling of unsteady fluid flow using neural network ensemblescited by 4
  4. 2024Differentiable programming across the PDE and Machine Learning barriercited by 4
  5. 2025Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learningcited by 2
  6. 2025Physics-informed Reduced Order Modeling of Time-dependent PDEs via Differentiable Solverscited by 2
  7. 2025mPOLICE: Provable Enforcement of Multi-Region Affine Constraints in Deep Neural Networkscited by 1
  8. 2025Beyond Blur: A Fluid Perspective on Generative Diffusion Modelscited by 1
  9. 2025GLUE: Coordinating Pre-Trained Generative Models for System-Level Designcited by 0
  10. 2026NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equationscited by 0
  11. 2026Mosaic: A Benchmark Suite for Differentiable Physics Solverscited by 0