Research output per year
Research output per year
Personal profile
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.
Education and Teaching
Teaching assistant for (5SC28) Machine learning for Systems and control.
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Deep Identification of Nonlinear Systems in Koopman Form
Iacob, L. C., Beintema, G. I., Schoukens, M. & Tóth, R., 1 Feb 2022, 60th IEEE Conference on Decision and Control, CDC 2021. Institute of Electrical and Electronics Engineers, p. 2288-2293 6 p. 9682946. (Proceedings of the IEEE Conference on Decision and Control; vol. 2021-December).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
7 Downloads (Pure) -
Identification of the nonlinear steering dynamics of an autonomous vehicle
Rödönyi, G., Beintema, G. I., Tóth, R., Schoukens, M., Pup, D., Kisari, Á., Vígh, Z., Korös, P., Soumelidis, A. & Bokor, J., 1 Jul 2021, In: IFAC-PapersOnLine. 54, 7, p. 708-713 6 p.Research output: Contribution to journal › Conference article › Academic › peer-review
Open AccessFile18 Downloads (Pure) -
Nonlinear state-space identification using deep encoder networks
Beintema, G. I., Tóth, R. & Schoukens, M., 2021, Proceedings of Learning for Dynamics and Control, 7-8 June 2021, The Cloud. Jadbabaie, A., Lygeros, J. & Pappas, G. J. (eds.). PMLR, p. 241-250 10 p. (Proceedings of Machine Learning Research; vol. 144).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
Open AccessFile35 Downloads (Pure) -
Non-linear State-space Model Identification from Video Data using Deep Encoders
Beintema, G. I., Schoukens, M. & Tóth, R., 2021.Research output: Contribution to conference › Paper › Academic
Open AccessFile34 Downloads (Pure) -
Comparison of Deep Learning Methods for System Identification
Beintema, G. I., Tóth, R. & Schoukens, M., 10 Mar 2020, 39th Benelux Meeting on Systems and Control: Book of Abstracts. Carloni, R., Jayawardhana, B. & Lefeber, E. (eds.). Groningen: University of Groningen, p. 157 1 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
Open AccessFile38 Downloads (Pure)
Activities
- 1 Conference
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17th European Turbulence Conference, ETC 2019
Gerben Beintema (Contributor)
3 Sep 2019 → 6 Sep 2019Activity: Participating in or organising an event types › Conference › Scientific
Thesis
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Controlling thermal convection via reinforcement learning
Author: Beintema, G. I., 2019Supervisor: Toschi, F. (Supervisor 1) & Corbetta, A. (Supervisor 2)
Student thesis: Master
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Quantitative analyses of role of the Shuttleworth equation in soft solids with validation by capillarity simulations
Author: Beintema, G. I., 2017Supervisor: van Brummelen, E. H. (Supervisor 1) & Snoeijer, J. H. (Supervisor 2)
Student thesis: Bachelor
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