| Controlling Rayleigh–Bénard convection via reinforcement learning G Beintema, A Corbetta, L Biferale, F Toschi Journal of Turbulence 21 (9-10), 585-605, 2020 | 95 | 2020 |
| Nonlinear state-space identification using deep encoder networks G Beintema, R Toth, M Schoukens Learning for dynamics and control, 241-250, 2021 | 51 | 2021 |
| Deep subspace encoders for nonlinear system identification GI Beintema, M Schoukens, R Tóth Automatica 156, 111210, 2023 | 27 | 2023 |
| 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 | 17 | 2021 |
| 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 | 15 | 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 | 15 | 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 | 12 | 2022 |
| 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 | 9 | 2021 |
| State derivative normalization for continuous-time deep neural networks J Weigand, GI Beintema, J Ulmen, D Görges, R Tóth, M Schoukens, ... arXiv preprint arXiv:2401.02902, 2024 | 3 | 2024 |
| 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 | 3 | 2023 |
| 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 | 3 | 2022 |
| Meta-state–space learning: An identification approach for stochastic dynamical systems GI Beintema, M Schoukens, R Tóth Automatica 167, 111787, 2024 | 1 | 2024 |
| Data–driven Learning of Nonlinear Dynamic Systems: A Deep Neural State–Space Approach GI Beintema | 1 | 2024 |
| Learning-Based Model-Augmentation of Nonlinear Approximative Models using the Sub-Space Encoder C Verhoek, GI Beintema, S Haesaert, M Schoukens, R Tóth 41st Benelux Meeting on Systems and Control 2022, 52-52, 2022 | 1 | 2022 |
| Reinforcement learning versus linear control of rayleigh-bénard convection A Corbetta, G Beintema, L Biferale, P Kumar, F Toschi APS Division of Fluid Dynamics Meeting Abstracts, P17. 006, 2019 | 1 | 2019 |
| Baseline Results for Selected Nonlinear System Identification Benchmarks MD Champneys, GI Beintema, R Tóth, M Schoukens, TJ Rogers arXiv preprint arXiv:2405.10779, 2024 | | 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 | | 2024 |
| Meta-state-space Identification of Stochastic Hearts GI Beintema, TB Schön, R Tóth, M Schoukens 43rd Benelux Meeting on Systems and Control, 2024 | | 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 | | 2024 |
| Output error port-Hamiltonian neural networks S Moradi, GI Beintema, NO Jaensson, R Tóth, M Schoukens 31st Workshop of the European Research Network on System Identification, 2023 | | 2023 |
Roland TothFull Professor, Eindhoven University of Technology在 tue.nl 的电子邮件经过验证
Roland TothFull Professor, Eindhoven University of Technology在 tue.nl 的电子邮件经过验证
Maarten SchoukensAssociate Professor, Eindhoven University of Technology (TU/e)在 tue.nl 的电子邮件经过验证