Year: 2025
Type: conference-paper
Abstract: 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.
Author: Yue Li
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414463072