3 citations · 3 across the 4 of their papers we have counts for
3 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…