GOAT: Learning Multi-Body Dynamics Using Graph Neural Network with Restrains

Accurately simulating physical processes is an extremely challenging task, but the rapid development of machine learning and the availability of large datasets have made Graph Neural Networks (GNNs) a powerful tool for effectively simulating physical systems. Currently, GNNs-based methods are primarily used in simple scenarios such as the free fall and collision of objects, fluid flow, and gravitational interactions among atoms. However, in complex industrial environments, there are always intricate interference factors such as friction, bearing connections, and torque affecting the motion between objects. Consequently, GNNs-based methods largely fail to solve practical physical problems related to complex multi-body dynamics. In this paper, to address the current lack of multi-body dynamics datasets in this field, we first introduce a multi-body dynamics dataset comprising eight different scenarios, each embodying distinct physical principles. Furthermore, we explore Graph Neural Simulators (GNSs) structure and physical priors and propose an efficient novel model, the Graph Neural Network with Restrgints (GOAT), that can directly learn the relationships between systems from multi-body trajectories, thereby enhancing performance. Our results have shown significant improvements compared to other state-of-the-art baselines, demonstrating strong generalization capabilities and data efficiency.