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.
View on arXiv@article{li2025_2504.14494,
title={ LBM-GNN: Graph Neural Network Enhanced Lattice Boltzmann Method },
author={ Yue Li },
journal={arXiv preprint arXiv:2504.14494},
year={ 2025 }
}