#machine learning
Sep 2025
Data-efficient Kernel Methods for Learning Hamiltonian Systems
This work proposes kernel-based methods for identifying and forecasting Hamiltonian systems directly from trajectory data, and provides a more general, problem-agnostic numerical framework that goes beyond Hamiltonian systems and can be used for data-driven learning of arbitrary dynamical systems.
Yasamin Jalalian, Mostafa Samir, B. Hamzi et al.
· arXiv.org · 4 citations