LBM-GNN: Graph Neural Network Enhanced Lattice Boltzmann Method
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Abstract (arXiv)
In this paper, we present LBM-GNN, a novel approach that enhances the traditional Lattice Boltzmann Method (LBM) with Graph Neural Networks (GNNs). We apply this method to fluid dynamics simulations, demonstrating improved stability and accuracy compared to standard LBM implementations. The method is validated using benchmark problems such as the Taylor-Green vortex, focusing on accuracy, conservation properties, and performance across different Reynolds numbers and grid resolutions. Our results indicate that GNN-enhanced LBM can maintain better conservation properties while improving numerical stability at higher Reynolds numbers.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 38 references that are in the map. First posted on arXiv 20 Apr 2025.
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7 of 38 references.
References · 7
- 2020Learning to Simulate Complex Physics with Graph Networkscited by 880
- 2021Machine learning accelerated computational fluid dynamicscited by 471
- 2020Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solverscited by 205
- 2018Deep Dynamical Modeling and Control of Unsteady Fluid Flowscited by 68
- 2024Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Trainingcited by 13
- 2025Physics-Informed Neural Networks for Enhanced Interface Preservation in Lattice Boltzmann Multiphase Simulationscited by 4
- 2025Federated Learning with Layer Skipping: Efficient Training of Large Language Models for Healthcare NLPcited by 2
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Citing papers per year: 2025: 2.
2 papers.