| Controlling Rayleigh–Bénard convection via reinforcement learning G Beintema, A Corbetta, L Biferale, F Toschi Journal of Turbulence 21 (9-10), 585-605, 2020 | 108 | 2020 |
| Nonlinear state-space identification using deep encoder networks G Beintema, R Toth, M Schoukens Learning for dynamics and control, 241-250, 2021 | 62 | 2021 |
| Deep subspace encoders for nonlinear system identification GI Beintema, M Schoukens, R Tóth Automatica 156, 111210, 2023 | 45 | 2023 |
| Deep-learning-based identification of LPV models for nonlinear systems C Verhoek, GI Beintema, S Haesaert, M Schoukens, R Tóth 2022 IEEE 61st Conference on Decision and Control (CDC), 3274-3280, 2022 | 21 | 2022 |
| Deep identification of nonlinear systems in koopman form LC Iacob, GI Beintema, M Schoukens, R Tóth 2021 60th IEEE Conference on Decision and Control (CDC), 2288-2293, 2021 | 21 | 2021 |
| Continuous-time identification of dynamic state-space models by deep subspace encoding GI Beintema, M Schoukens, R Tóth arXiv preprint arXiv:2204.09405, 2022 | 20 | 2022 |
| Non-linear state-space model identification from video data using deep encoders GI Beintema, R Toth, M Schoukens IFAC-PapersOnLine 54 (7), 697-701, 2021 | 17 | 2021 |
| Identification of the nonlinear steering dynamics of an autonomous vehicle G Rödönyi, GI Beintema, R Tóth, M Schoukens, D Pup, Á Kisari, Z Vígh, ... IFAC-PapersOnLine 54 (7), 708-713, 2021 | 13 | 2021 |
| Computationally efficient predictive control based on ANN state-space models JH Hoekstra, B Cseppento, GI Beintema, M Schoukens, Z Kollár, R Tóth 2023 62nd IEEE Conference on Decision and Control (CDC), 6336-6341, 2023 | 6 | 2023 |
| State derivative normalization for continuous-time deep neural networks J Weigand, GI Beintema, J Ulmen, D Görges, R Tóth, M Schoukens, ... IFAC-PapersOnLine 58 (15), 253-258, 2024 | 4 | 2024 |
| NARX identification using derivative-based regularized neural networks LH Peeters, GI Beintema, M Forgione, M Schoukens 2022 IEEE 61st Conference on Decision and Control (CDC), 1515-1520, 2022 | 4 | 2022 |
| Data–driven Learning of Nonlinear Dynamic Systems: A Deep Neural State–Space Approach GI Beintema | 2 | 2024 |
| Meta-state–space learning: An identification approach for stochastic dynamical systems GI Beintema, M Schoukens, R Tóth Automatica 167, 111787, 2024 | 1 | 2024 |
| Output error port-hamiltonian neural networks: Cascaded tanks system with overflow S Moradi, GI Beintema, NO Jaensson, R Tóth, M Schoukens Workshop on Nonlinear System Identification Benchmarks, 2024 | 1 | 2024 |
| Deep learning of vehicle dynamics M Szécsi, B Györök, Á Weinhardt-Kovács, GI Beintema, M Schoukens, ... IFAC-PapersOnLine 58 (15), 283-288, 2024 | 1 | 2024 |
| Baseline Results for Selected Nonlinear System Identification Benchmarks M Champneys, GI Beintema, R Tóth, M Schoukens, TJ Rogers IFAC-PapersOnLine 58 (15), 474-479, 2024 | 1 | 2024 |
| Learning-based augmentation of physics-based models: an industrial robot use case A Retzler, R Tóth, M Schoukens, GI Beintema, J Weigand, JP Noël, ... Data-Centric Engineering 5, e12, 2024 | 1 | 2024 |
| Output error port-hamiltonian neural network S Moradi, GI Beintema, NO Jaensson, R Tóth, M Schoukens 31st Workshop of the European Research Network on System Identification, 2023 | 1 | 2023 |
| Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems GI Beintema, M Schoukens, R Tóth arXiv preprint arXiv:2307.06675, 2023 | 1 | 2023 |
| Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach R Ramkannan, GI Beintema, R Tóth, M Schoukens IFAC-PapersOnLine 56 (2), 5146-5151, 2023 | 1 | 2023 |
Roland TothFull Professor, Eindhoven University of TechnologyEmail verificata su tue.nl