Title: Learning Hamiltonian Dynamics at Scale:  A Differential-Geometric Approach
Primary area: learning on time series and dynamical systems
Keywords: physics-inspired networks, dynamics learning, model-order reduction, Hamiltonian dynamics, symplectic geometry, Riemannian geometry

Abstract: By embedding physical intuition, network architectures enforce fundamental properties, such as energy conservation laws, leading to plausible predictions. Yet, scaling these models to intrinsically high-dimensional systems remains a significant challenge. This paper introduces Geometric Reduced-order Hamiltonian Neural Network (RO-HNN), a novel physics-inspired neural network that combines the conservation laws of Hamiltonian mechanics with the scalability of model order reduction. RO-HNN is built on two core components: a novel geometrically-constrained symplectic autoencoder that learns a low-dimensional, structure-preserving symplectic submanifold, and a geometric Hamiltonian neural network that models the dynamics on the submanifold. Our experiments demonstrate that RO-HNN provides physically-consistent, stable, and generalizable predictions of complex high-dimensional dynamics, thereby effectively extending the scope of Hamiltonian neural networks to high-dimensional physical systems.

TL;DR: a novel physics-inspired neural network that combines the conservations laws of Hamiltonian mechanics with the scalability of model order reduction.