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ORCIDStefan Klus
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2020 – today
- 2025
[i24]Stefan Klus, Joel-Pascal N'Konzi:
Data-driven system identification using quadratic embeddings of nonlinear dynamics. CoRR abs/2501.08202 (2025)- 2024
- [i23]Liam Llamazares-Elias, Samir Llamazares-Elías, Jonas Latz, Stefan Klus:
Data-driven approximation of Koopman operators and generators: Convergence rates and error bounds. CoRR abs/2405.00539 (2024) - [i22]Maia Trower, Natasa Djurdjevac Conrad, Stefan Klus:
Clustering Time-Evolving Networks Using the Dynamic Graph Laplacian. CoRR abs/2407.12864 (2024) - [i21]Mohammad Tabish, Neil K. Chada, Stefan Klus:
Learning dynamical systems from data: Gradient-based dictionary optimization. CoRR abs/2411.04775 (2024) - [i20]Filip Blaskovic, Tim O. F. Conrad, Stefan Klus, Natasa Djurdjevac Conrad:
Clustering Time-Snapshots of Temporal Networks: From Synthetic Data to Real-World Applications. CoRR abs/2412.12187 (2024) - 2023
- [j18]Christof Schütte
, Stefan Klus, Carsten Hartmann:
Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning. Acta Numer. 32: 517-673 (2023) - [j17]Stefan Klus, Natasa Djurdjevac Conrad:
Koopman-Based Spectral Clustering of Directed and Time-Evolving Graphs. J. Nonlinear Sci. 33(1): 8 (2023) - [i19]Stefan Klus, Maia Trower:
Transfer operators on graphs: Spectral clustering and beyond. CoRR abs/2305.11766 (2023) - [i18]Stefan Klus, Patrick Gelß:
Continuous optimization methods for the graph isomorphism problem. CoRR abs/2311.16912 (2023) - 2022
- [j16]Mattes Mollenhauer, Stefan Klus, Christof Schütte, Péter Koltai:
Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence. J. Mach. Learn. Res. 23: 327:1-327:34 (2022) - [j15]Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, J. Nathan Kutz, Steven L. Brunton, Frank Noé:
Deeptime: a Python library for machine learning dynamical models from time series data. Mach. Learn. Sci. Technol. 3(1): 15009 (2022) - [c2]Hongyu Zhu, Stefan Klus, Tuhin Sahai:
A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphs. CCTA 2022: 1202-1207 - [i17]Hongyu Zhu, Stefan Klus, Tuhin Sahai:
A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphs. CoRR abs/2203.00004 (2022) - 2021
- [j14]Andreas Bittracher, Stefan Klus, Boumediene Hamzi, Péter Koltai, Christof Schütte:
Dimensionality Reduction of Complex Metastable Systems via Kernel Embeddings of Transition Manifolds. J. Nonlinear Sci. 31(1): 3 (2021) - [j13]Patrick Gelß, Stefan Klus, Ingmar Schuster, Christof Schütte:
Feature space approximation for kernel-based supervised learning. Knowl. Based Syst. 221: 106935 (2021) - [j12]Stefan Klus, Patrick Gelß, Feliks Nüske, Frank Noé:
Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry. Mach. Learn. Sci. Technol. 2(4): 45016 (2021) - [i16]Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, J. Nathan Kutz, Steven L. Brunton, Frank Noé:
Deeptime: a Python library for machine learning dynamical models from time series data. CoRR abs/2110.15013 (2021) - 2020
- [j11]Kateryna Melnyk, Stefan Klus, Grégoire Montavon, Tim O. F. Conrad:
GraphKKE: graph Kernel Koopman embedding for human microbiome analysis. Appl. Netw. Sci. 5(1): 96 (2020) - [j10]Stefan Klus, Feliks Nüske, Boumediene Hamzi:
Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator. Entropy 22(7): 722 (2020) - [j9]Stefan Klus, Ingmar Schuster, Krikamol Muandet:
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. J. Nonlinear Sci. 30(1): 283-315 (2020) - [c1]Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet:
Kernel Conditional Density Operators. AISTATS 2020: 993-1004 - [i15]Mattes Mollenhauer, Stefan Klus, Christof Schütte, Péter Koltai:
Kernel autocovariance operators of stationary processes: Estimation and convergence. CoRR abs/2004.00891 (2020) - [i14]Stefan Klus, Feliks Nüske, Boumediene Hamzi:
Kernel-based approximation of the Koopman generator and Schrödinger operator. CoRR abs/2005.13231 (2020) - [i13]Kateryna Melnyk, Stefan Klus, Grégoire Montavon, Tim O. F. Conrad:
GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis. CoRR abs/2008.05903 (2020) - [i12]Patrick Gelß, Stefan Klus, Ingmar Schuster, Christof Schütte:
Feature space approximation for kernel-based supervised learning. CoRR abs/2011.12651 (2020)
2010 – 2019
- 2019
- [j8]Stefan Klus, Patrick Gelß:
Tensor-Based Algorithms for Image Classification. Algorithms 12(11): 240 (2019) - [j7]Sebastian Peitz, Stefan Klus:
Koopman operator-based model reduction for switched-system control of PDEs. Autom. 106: 184-191 (2019) - [j6]Wei Zhang, Stefan Klus, Tim O. F. Conrad, Christof Schütte:
Learning Chemical Reaction Networks from Trajectory Data. SIAM J. Appl. Dyn. Syst. 18(4): 2000-2046 (2019) - [i11]Ingmar Schuster, Mattes Mollenhauer, Stefan Klus, Krikamol Muandet:
Kernel Conditional Density Operators. CoRR abs/1905.11255 (2019) - [i10]Feliks Nüske, Patrick Gelß, Stefan Klus, Cecilia Clementi:
Tensor-based EDMD for the Koopman analysis of high-dimensional systems. CoRR abs/1908.04741 (2019) - [i9]Stefan Klus, Patrick Gelß:
Tensor-based algorithms for image classification. CoRR abs/1910.02150 (2019) - 2018
- [j5]Andreas Bittracher, Péter Koltai, Stefan Klus, Ralf Banisch, Michael Dellnitz, Christof Schütte:
Transition Manifolds of Complex Metastable Systems - Theory and Data-Driven Computation of Effective Dynamics. J. Nonlinear Sci. 28(2): 471-512 (2018) - [j4]Stefan Klus, Feliks Nüske, Péter Koltai, Hao Wu, Ioannis G. Kevrekidis, Christof Schütte, Frank Noé:
Data-Driven Model Reduction and Transfer Operator Approximation. J. Nonlinear Sci. 28(3): 985-1010 (2018) - [i8]Stefan Klus, Sebastian Peitz, Ingmar Schuster:
Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions. CoRR abs/1805.10118 (2018) - [i7]Stefan Klus, Andreas Bittracher, Ingmar Schuster, Christof Schütte:
A kernel-based approach to molecular conformation analysis. CoRR abs/1809.11092 (2018) - 2017
- [j3]Patrick Gelß, Stefan Klus, Sebastian Matera, Christof Schütte:
Nearest-neighbor interaction systems in the tensor-train format. J. Comput. Phys. 341: 140-162 (2017) - [j2]Michael Dellnitz, Stefan Klus, Adrian Ziessler:
A Set-Oriented Numerical Approach for Dynamical Systems with Parameter Uncertainty. SIAM J. Appl. Dyn. Syst. 16(1): 120-138 (2017) - [i6]Tuhin Sahai, Stefan Klus, Michael Dellnitz:
Continuous Relaxations for the Traveling Salesman Problem. CoRR abs/1702.05224 (2017) - [i5]Stefan Klus, Ingmar Schuster, Krikamol Muandet:
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. CoRR abs/1712.01572 (2017) - 2015
- [i4]Slaven Peles, Stefan Klus:
Sparse Automatic Differentiation for Large-Scale Computations Using Abstract Elementary Algebra. CoRR abs/1505.00838 (2015) - 2014
- [i3]Stefan Klus, Tuhin Sahai:
A Spectral Assignment Approach for the Graph Isomorphism Problem. CoRR abs/1411.0969 (2014) - 2012
- [i2]Tuhin Sahai, Stefan Klus, Michael Dellnitz:
A Traveling Salesman Learns Bayesian Networks. CoRR abs/1211.4888 (2012) - 2011
- [j1]Stefan Klus, Tuhin Sahai, Cong Liu, Michael Dellnitz:
An efficient algorithm for the parallel solution of high-dimensional differential equations. J. Comput. Appl. Math. 235(9): 3053-3062 (2011) - 2010
- [i1]Stefan Klus, Tuhin Sahai, Cong Liu, Michael Dellnitz:
An efficient algorithm for the parallel solution of high-dimensional differential equations. CoRR abs/1003.5238 (2010)
Coauthor Index
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