Stefan Klus
Zitiert von
Zitiert von
On the numerical approximation of the Perron-Frobenius and Koopman operator
S Klus, P Koltai, C Schütte
arXiv preprint arXiv:1512.05997, 2015
On the numerical approximation of the Perron-Frobenius and Koopman operator
S Klus, P Koltai, C Schütte
arXiv preprint arXiv:1512.05997, 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
Koopman operator-based model reduction for switched-system control of PDEs
S Peitz, S Klus
Automatica 106, 184-191, 2019
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
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), 154104, 2017
Eigendecompositions of transfer operators in reproducing kernel Hilbert spaces
S Klus, I Schuster, K Muandet
Journal of Nonlinear Science 30, 283-315, 2020
Tensor-based dynamic mode decomposition
S Klus, P Gelß, S Peitz, C Schütte
Nonlinearity 31 (7), 3359, 2018
Multidimensional approximation of nonlinear dynamical systems
P Gelß, S Klus, J Eisert, C Schütte
Journal of Computational and Nonlinear Dynamics 14 (6), 2019
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
Kernel-based approximation of the Koopman generator and Schrödinger operator
S Klus, F Nüske, B Hamzi
Entropy 22 (7), 722, 2020
Tensor-based algorithms for image classification
S Klus, P Gelß
Algorithms 12 (11), 240, 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
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
A kernel-based approach to molecular conformation analysis
S Klus, A Bittracher, I Schuster, C Schütte
The Journal of Chemical Physics 149 (24), 244109, 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), 123112, 2019
Kernel conditional density operators
I Schuster, M Mollenhauer, S Klus, K Muandet
International conference on artificial intelligence and statistics, 993-1004, 2020
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
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
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
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