55 citations · 137 across the 38 of their papers we have counts for
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Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems
Gerben I. Beintema, Maarten Schoukens, Roland Tóth
Available methods for identification of stochastic dynamical systems from input-output data generally impose restricting structural assumptions on either the noise structure in the…
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models
Sarvin Moradi, Nick Jaensson, Roland Tóth +1
In order to make data-driven models of physical systems interpretable and reliable, it is essential to include prior physical knowledge in the modeling framework. Hamiltonian Neura…
Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach
Rishi Ramkannan, Gerben I. Beintema, Roland Tóth +1
The SUBNET neural network architecture has been developed to identify nonlinear state-space models from input-output data. To achieve this, it combines the rolled-out nonlinear sta…
Computationally efficient predictive control based on ANN state-space models
Jan H. Hoekstra, Bence Cseppentő, Gerben I. Beintema +3
Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models. However, if the resulting mode…
Finite Dimensional Koopman Form of Polynomial Nonlinear Systems
Lucian Cristian Iacob, Maarten Schoukens, Roland Tóth
The Koopman framework is a popular approach to transform a finite dimensional nonlinear system into an infinite dimensional, but linear model through a lifting process, using so-ca…