55 citations · 137 across the 38 of their papers we have counts for
6 papers · 1 filter
Orthogonal-by-construction augmentation of physics-based input-output models
Bendegúz M. Györök, Maarten Schoukens, Tamás Péni +1
This paper proposes a novel orthogonal-by-construction parametrization for augmenting physics-based input-output models with a learning component in an additive sense. The parametr…
Exact Finite Koopman Embedding of Block-Oriented Polynomial Systems
Lucian Cristian Iacob, Roland Tóth, Maarten Schoukens
The challenge of finding exact and finite-dimensional Koopman embeddings of nonlinear systems has been largely circumvented by employing data-driven techniques to learn models of d…
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…
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…
On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach
Yuhan Liu, Máté Kiss, Roland Tóth +1
While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems where robustness to extrapolation/interpolation errors…
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…