LBM-GNN: Graph Neural Network Enhanced Lattice Boltzmann Method
This is an incremental improvement for computational fluid dynamics researchers, addressing stability issues in simulations.
The paper tackles fluid dynamics simulations by enhancing the Lattice Boltzmann Method with Graph Neural Networks, resulting in improved stability and accuracy, with better conservation properties at higher Reynolds numbers.
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.