Proceedings of the
The 33rd European Safety and Reliability Conference (ESREL 2023)
3 – 8 September 2023, Southampton, UK
Learning Dynamics of Spring-Mass Models with Physics-Informed Graph Neural Networks
1Laboratory of Intelligent Maintenance and Operation systems, Switzerland.
2Computational Mechanics Group.
ABSTRACT
We propose a physics-informed message-passing graph neural network (GNN) for learning the dynamics of springmass systems. The proposed method embeds the underlying physics directly into the message-passing scheme of the GNN. We compare the new scheme with conventional message passing and demonstrate the generalization capability of the method. Additionally, we infer the learned parameters of the edges and show that these parameters serve as explainable metrics for the learned physics. The numerical results indicate that the proposed method accurately learns the physics of the spring-mass systems.
Keywords: Graph networks, Physics simulations, Coupled-spring mass systems.
