Learning partially observed systems with neural Hamiltonian ordinary differential equations

ArXi:2605.23510v1 Announce Type: new When learning dynamical systems from data, embedding physical structure can constrain the solution space and improve generalization, but many physics-informed models assume access to the full system state. This limits their use in partially observed settings, where some state variables are completely unobserved and must be inferred without direct supervision.