55 citations · 137 across the 38 of their papers we have counts for
4 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…
Orthogonal projection-based regularization for efficient model augmentation
Bendegúz M. Györök, Jan H. Hoekstra, Johan Kon +3
Deep-learning-based nonlinear system identification has shown the ability to produce reliable and highly accurate models in practice. However, these black-box models lack physical…
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…