| On the numerical approximation of the Perron-Frobenius and Koopman operator S Klus, P Koltai, C Schütte arXiv preprint arXiv:1512.05997, 2015 | 332 | 2015 |
| Data-driven model reduction and transfer operator approximation S Klus, F Nüske, P Koltai, H Wu, I Kevrekidis, C Schütte, F Noé Journal of Nonlinear Science 28, 985-1010, 2018 | 297 | 2018 |
| Data-driven approximation of the Koopman generator: Model reduction, system identification, and control S Klus, F Nüske, S Peitz, JH Niemann, C Clementi, C Schütte Physica D: Nonlinear Phenomena 406, 132416, 2020 | 226 | 2020 |
| Koopman operator-based model reduction for switched-system control of PDEs S Peitz, S Klus Automatica 106, 184-191, 2019 | 208 | 2019 |
| Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations H Wu, F Nüske, F Paul, S Klus, P Koltai, F Noé The Journal of chemical physics 146 (15), 2017 | 139 | 2017 |
| Eigendecompositions of transfer operators in reproducing kernel Hilbert spaces S Klus, I Schuster, K Muandet Journal of Nonlinear Science 30, 283-315, 2020 | 132 | 2020 |
| Tensor-based dynamic mode decomposition S Klus, P Gelß, S Peitz, C Schütte Nonlinearity 31 (7), 3359, 2018 | 97 | 2018 |
| Multidimensional approximation of nonlinear dynamical systems P Gelß, S Klus, J Eisert, C Schütte Journal of Computational and Nonlinear Dynamics 14 (6), 061006, 2019 | 83 | 2019 |
| Deeptime: a Python library for machine learning dynamical models from time series data M Hoffmann, M Scherer, T Hempel, A Mardt, B de Silva, BE Husic, S Klus, ... Machine Learning: Science and Technology 3 (1), 015009, 2021 | 79 | 2021 |
| Kernel-based approximation of the Koopman generator and Schrödinger operator S Klus, F Nüske, B Hamzi Entropy 22 (7), 722, 2020 | 53 | 2020 |
| Transition manifolds of complex metastable systems: Theory and data-driven computation of effective dynamics A Bittracher, P Koltai, S Klus, R Banisch, M Dellnitz, C Schütte Journal of nonlinear science 28, 471-512, 2018 | 49 | 2018 |
| Tensor-based algorithms for image classification S Klus, P Gelß Algorithms 12 (11), 240, 2019 | 38 | 2019 |
| Koopman operator-based finite-control-set model predictive control for electrical drives S Hanke, S Peitz, O Wallscheid, S Klus, J Böcker, M Dellnitz arXiv preprint arXiv:1804.00854, 2018 | 33 | 2018 |
| Kernel methods for detecting coherent structures in dynamical data S Klus, BE Husic, M Mollenhauer, F Noé Chaos: An Interdisciplinary Journal of Nonlinear Science 29 (12), 2019 | 32 | 2019 |
| Kernel conditional density operators I Schuster, M Mollenhauer, S Klus, K Muandet International Conference on Artificial Intelligence and Statistics, 993-1004, 2020 | 28 | 2020 |
| A kernel-based approach to molecular conformation analysis S Klus, A Bittracher, I Schuster, C Schütte The Journal of Chemical Physics 149 (24), 2018 | 27 | 2018 |
| Dimensionality reduction of complex metastable systems via kernel embeddings of transition manifolds A Bittracher, S Klus, B Hamzi, P Koltai, C Schütte Journal of Nonlinear Science 31, 1-41, 2021 | 26 | 2021 |
| Nearest-neighbor interaction systems in the tensor-train format P Gelß, S Klus, S Matera, C Schütte Journal of Computational Physics 341, 140-162, 2017 | 24 | 2017 |
| Feedback control of nonlinear PDEs using data-efficient reduced order models based on the Koopman operator S Peitz, S Klus The Koopman Operator in Systems and Control: Concepts, Methodologies, and …, 2020 | 22 | 2020 |
| A set-oriented numerical approach for dynamical systems with parameter uncertainty M Dellnitz, S Klus, A Ziessler SIAM Journal on Applied Dynamical Systems 16 (1), 120-138, 2017 | 21 | 2017 |
Feliks NüskeMax Planck Institute for Dynamics of Complex Technical Systems, MagdeburgBestätigte E-Mail-Adresse bei mpi-magdeburg.mpg.de
