Graph-DEM: A Graph Neural Network Model for Proxy and Acceleration Discrete Element Method

The discrete element method (DEM) is widely employed in various fields for analyzing rock and soil movement. However, the traditional DEM involves a large number of calculations, which leads to reduced computational efficiency. Deep-learning presents a promising solution to this issue by utilizing neural networks to approximate DEM calculations. Moreover, the consistency between the arrangement of discrete particles and the structure presented in graph neural networks further reinforces the validity of this approach. In this study, we propose a novel model called Graph-DEM based on graph neural networks, which significantly enhances the speed of DEM calculations. Meanwhile, our model demonstrates the capability of adaptive learning across various constitutive relationships. To evaluate the model’s performance, we measure particle-trajectory prediction accuracy on three scenario datasets (dynamic, static, and principle experiments) and on two public datasets. In addition, the computational efficiency of the Graph-DEM model are compared against the traditional DEM. The experimental results demonstrate the superiority of the model in terms of accuracy, universality, and computational efficiency.