2 papers
cs.LG2026
Learning partially observed systems with neural Hamiltonian ordinary differential equations
Sunniva Meltzer, Sølve Eidnes, Alexander Johannes Stasik
When learning dynamical systems from data, embedding physical structure can constrain the solution space and improve generalization, but many physics-informed models assume access…
cs.LG2026
A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting
Brooks Kinch, Xiaozhe Hu, Yilong Huang +6
For autoregressive modeling of chaotic dynamical systems over long time horizons, the stability of both training and inference is a major challenge in building scientific foundatio…