Improving Hamiltonian Learning through Local Energy Decomposition

Hamiltonian neural networks provide a principled framework for learning conservative dynamics by parameterizing a scalar Hamiltonian and deriving the vector field through Hamilton's equations. However, preserving Hamiltonian structure does not guarantee that the energy function itself is represented accurately. When the Hamiltonian is modeled by a conventional multilayer perceptron, it inherits the spectral bias of coordinate-based neural representations, learning smooth components more readily than fine-scale variations that can strongly affect the dynamics after differentiation. We introduce the Local Multiscale Hamiltonian Neural Network (LM-HNN), which restructures the Hamiltonian according to its physical interaction structure. LM-HNN decomposes the energy into kinetic and potential components and represents them as sums of shared local functions over multiple interaction neighborhoods. This replaces a single high-dimensional approximation problem with lower-dimensional, reusable interaction laws, making fine-scale energy structure easier to resolve while preserving Hamiltonian dynamics. Across low-dimensional, interacting, and fast–slow systems, LM-HNN consistently achieves more accurate Hamiltonian derivatives and long-horizon rollouts than competing structure-preserving models, while substantially extending the effective spectral range of the learned energy. The same structured representation also generalizes more reliably to energy levels not observed during training. These results show that accuracy depends on the Hamiltonian representation as well as its structure, and that incorporating locality, decomposition, and shared interaction laws provides a simple and effective route to more accurate and generalizable Hamiltonian learning.