2 papers
eess.SY2026
Learning Koopman Models From Data Under General Noise Conditions
Lucian Cristian Iacob, Máté Szécsi, Gerben Izaak Beintema +2
This paper presents a novel identification approach of Koopman models of nonlinear systems with inputs under rather general noise conditions. The method uses deep state-space encod…
cs.LG2025
Port-Hamiltonian Neural Networks with Output Error Noise Models
Sarvin Moradi, Gerben I. Beintema, Nick Jaensson +2
Hamiltonian neural networks (HNNs) represent a promising class of physics-informed deep learning methods that utilize Hamiltonian theory as foundational knowledge within neural net…