Maarten Schoukens
Department / Institute
RESEARCH PROFILE
Maarten Schoukens is an Assistant Professor in the Control Systems (CS) Group at the Department of Electrical Engineering, Eindhoven University of Technology. His main research interests include the measurement and data-driven modeling of nonlinear dynamical and linear parameter-varying systems using system identification and machine learning techniques, experimental design, and frequency domain methods in identification. Maarten was awarded an FWO Ph.D. Fellowship in 2011, an EU - Marie Skłodowska-Curie Individual Fellowship in 2018, and an ERC Starting Grant in 2022. Maarten is active as an associate editor for the IEEE CSS (CDC, ACC) and the ECC Conference Editorial Board, and has been active as an associate editor for multiple IFAC conferences. Finally, Maarten is a co-organizer of the nonlinearbenchmark.org initiative promoting the use of common datasets in data-driven nonlinear dynamical modeling research.
ACADEMIC BACKGROUND
Maarten Schoukens received the master’s degree in electrical engineering and the Ph.D. degree in engineering from the Vrije Universiteit Brussel (VUB), Brussels, Belgium, in 2010 and 2015 respectively. From 2015 to 2017, he has been a Post-Doctoral Researcher with the ELEC Department, VUB. In October 2017 he joined the Control Systems research group, TU/e, Eindhoven, The Netherlands as a Post-Doctoral Researcher, since 2018 he is an Assistant Professor in the same group.
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) -
Dhruv Khandelwal,Maarten Schoukens,Roland Tóth
Automated multi-objective system identification using grammar-based genetic programming
Automatica (2023) -
Sarvin Moradi,Gerben Beintema,Nick Jaensson,Roland Tóth,Maarten Schoukens
Output error port Hamiltonian neural networks
(2023) -
Sarvin Moradi,Nick Jaensson,Roland Tóth,Maarten Schoukens
Learning physical models using Hamiltonian Neural Networks with output error noise models
(2023)
Ancillary Activities
No ancillary activities