14 citations · 30 across the 14 of their papers we have counts for
3 papers · 1 filter
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…
Continuous-time identification of dynamic state-space models by deep subspace encoding
Gerben I. Beintema, Maarten Schoukens, Roland Tóth
Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time…
Nonlinear state-space identification using deep encoder networks
Gerben Beintema, Roland Toth, Maarten Schoukens
Nonlinear state-space identification for dynamical systems is most often performed by minimizing the simulation error to reduce the effect of model errors. This optimization proble…