A physical-information-flow-constrained temporal graph neural network-based simulator for granular materials

Abstract

This paper introduces the Temporal Graph Neural Network-based Simulator (TGNNS), a novel physical-information-flow-constrained deep learning-based simulator for granular material modeling. The TGNNS leverages a series of frames, each representing material point positions, enabling particle dynamics to propagate through the sequence, resulting in a more physically grounded architecture for granular flow learning. The TGNNS has been thoroughly trained, validated, and tested using simulation data derived from a hierarchical multiscale modeling approach, DEMPM, which combines the Material Point Method (MPM) and the Discrete Element Method (DEM). Results demonstrate that the TGNNS performs robustly with previously unseen datasets of varying granular column sizes, even under manually incorporated barrier boundary conditions. Remarkably, the TGNNS operates at a speed 100 times faster than direct numerical simulation using the state-of-the-art GPU-based DEMPM. Employing a unique deep learning architecture that is constrained by the flow of physical information, the TGNNS offers a pioneering learning paradigm for multiscale emerging behaviors of granular materials and provides a potential solution to physics-based modeling in digital twins involving granular materials.