Gerben Beintema
Department / Institute
RESEARCH PROFILE
Gerben I. Beintema is a Doctoral Candidate at the Control Systems (CS) Group at the Department of Electrical Engineering. His current research is on the intersection of nonlinear system identification and deep learning, under the supervision of Associate Professor Roland Tóth and assistant Professor Maarten Schoukens. His main research interest is improving and understanding deep nonlinear state-space identification to obtain interpretable, robust, and generally applicable methods.
ACADEMIC BACKGROUND
Gerben I. Beintema obtained his BSc degree in Applied Physics (Flow profile) from the Eindhoven University of Technology (TU/e), cum laude, in 2019. Under the supervision, of Full Professor Federico Toschi, his MSc thesis showed that deep reinforcement learning can be used to obtain state-of-the-art controllers to reduce fluid movement in a chaotic thermally driven convective flow. This reduction of flow is a desirable property for crystal silicon growth. This research has been successfully published in the peer-reviewed Journal of Turbulence.
Recent Publications
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G.I. Beintema,Maarten Schoukens,Roland Tóth
Deep subspace encoders for nonlinear system identification
Automatica (2023) -
rerum
Output Error Port-Hamiltonian Neural Network
(2023) -
Sarvin Moradi,Gerben Beintema,Nick Jaensson,Roland Tóth,Maarten Schoukens
Output error port Hamiltonian neural networks
(2023) -
Chris Verhoek,Gerben I. Beintema,Sofie Haesaert,Maarten Schoukens,Roland Tóth
Deep-Learning-Based Identification of LPV Models for Nonlinear Systems
(2023) -
G.I. Beintema,M. Forgione,M. Schoukens
NARX Identification using Derivative-Based Regularized Neural Networks
(2023)
Ancillary Activities
No ancillary activities