Learning Dynamics of Spring-Mass Models with Physics-Informed Graph Neural Networks

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.