Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach

ArXi:2509.24627v2 Announce Type: replace Embedding physical intuition into network architectures allows the learning of dynamics that enforce fundamental properties, such as energy conservation laws, thereby leading to physically-plausible predictions. Yet, scaling these models to high-dimensional dynamical systems remains a significant challenge. This paper