Learning the Dynamics of Physical Systems with Hamiltonian Graph Neural Networks

Inductive biases in the form of conservation laws have been shown to provide superior performance for modeling physical systems. Here, we present Hamiltonian graph neural network (HGNN), a physics-informed GNN that learns the dynamics directly from the trajectory. We evaluate the performance of HGNN on spring, pendulum, and gravitational systems and show that it outperforms other Hamiltonian-based neural networks. We also demonstrate the zero-shot generalizability of HGNN to unseen hybrid spring-pendulum systems and system sizes that are two orders of magnitude larger than the training systems. HGNN provides excellent inference in all the systems providing a stable trajectory. Altogether, HGNN presents a promising approach to modeling complex physical systems directly from their trajectory.