An efficient algorithm for the parallel solution of high-dimensional differential equations. - Stefan Klus; Tuhin Sahai; Cong Liu; Michael Dellnitz (J. Comput. Appl. Math., 2011)

A Traveling Salesman Learns Bayesian Networks - Tuhin Sahai; Stefan Klus; Michael Dellnitz (CoRR, 2012)

A Spectral Assignment Approach for the Graph Isomorphism Problem. - Stefan Klus; Tuhin Sahai (CoRR, 2014)

Sparse Automatic Differentiation for Large-Scale Computations Using Abstract Elementary Algebra. - Slaven Peles; Stefan Klus (CoRR, 2015)

Continuous Relaxations for the Traveling Salesman Problem. - Tuhin Sahai; Stefan Klus; Michael Dellnitz (CoRR, 2017)

Nearest-neighbor interaction systems in the tensor-train format. - Patrick Gelß; Stefan Klus; Sebastian Matera; Christof Schütte (J. Comput. Phys., 2017)

A Set-Oriented Numerical Approach for Dynamical Systems with Parameter Uncertainty. - Michael Dellnitz; Stefan Klus; Adrian Ziessler (SIAM J. Appl. Dyn. Syst., 2017)

Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions. - Stefan Klus; Sebastian Peitz; Ingmar Schuster (CoRR, 2018)

A kernel-based approach to molecular conformation analysis. - Stefan Klus; Andreas Bittracher; Ingmar Schuster; Christof Schütte (CoRR, 2018)

Transition Manifolds of Complex Metastable Systems - Theory and Data-Driven Computation of Effective Dynamics. - Andreas Bittracher; Péter Koltai; Stefan Klus; Ralf Banisch; Michael Dellnitz; Christof Schütte (J. Nonlinear Sci., 2018)

Data-Driven Model Reduction and Transfer Operator Approximation. - Stefan Klus; Feliks Nüske; Péter Koltai; Hao Wu 035; Ioannis G. Kevrekidis; Christof Schütte; Frank Noé (J. Nonlinear Sci., 2018)

Tensor-Based Algorithms for Image Classification. - Stefan Klus; Patrick Gelß (Algorithms, 2019)

Koopman operator-based model reduction for switched-system control of PDEs. - Sebastian Peitz; Stefan Klus (Autom., 2019)

Tensor-based EDMD for the Koopman analysis of high-dimensional systems. - Feliks Nüske; Patrick Gelß; Stefan Klus; Cecilia Clementi (CoRR, 2019)

Learning Chemical Reaction Networks from Trajectory Data. - Wei Zhang 065; Stefan Klus; Tim O. F. Conrad; Christof Schütte (SIAM J. Appl. Dyn. Syst., 2019)

Kernel Conditional Density Operators. - Ingmar Schuster; Mattes Mollenhauer; Stefan Klus; Krikamol Muandet (AISTATS, 2020)

GraphKKE: graph Kernel Koopman embedding for human microbiome analysis. - Kateryna Melnyk; Stefan Klus; Grégoire Montavon; Tim O. F. Conrad (Appl. Netw. Sci., 2020)

Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator. - Stefan Klus; Feliks Nüske; Boumediene Hamzi (Entropy, 2020)

Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. - Stefan Klus; Ingmar Schuster; Krikamol Muandet (J. Nonlinear Sci., 2020)

Dimensionality Reduction of Complex Metastable Systems via Kernel Embeddings of Transition Manifolds. - Andreas Bittracher; Stefan Klus; Boumediene Hamzi; Péter Koltai; Christof Schütte (J. Nonlinear Sci., 2021)

Feature space approximation for kernel-based supervised learning. - Patrick Gelß; Stefan Klus; Ingmar Schuster; Christof Schütte (Knowl. Based Syst., 2021)

Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry. - Stefan Klus; Patrick Gelß; Feliks Nüske; Frank Noé (Mach. Learn. Sci. Technol., 2021)

A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphs. - Hongyu Zhu; Stefan Klus; Tuhin Sahai (CCTA, 2022)

Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence. - Mattes Mollenhauer; Stefan Klus; Christof Schütte; Péter Koltai (J. Mach. Learn. Res., 2022)

Deeptime: a Python library for machine learning dynamical models from time series data. - Moritz Hoffmann 002; Martin Scherer; Tim Hempel; Andreas Mardt; Brian de Silva; Brooke E. Husic; Stefan Klus; Hao Wu 035; J. Nathan Kutz; Steven L. Brunton; Frank Noé (Mach. Learn. Sci. Technol., 2022)

Transfer operators on graphs: Spectral clustering and beyond. - Stefan Klus; Maia Trower (CoRR, 2023)

Continuous optimization methods for the graph isomorphism problem. - Stefan Klus; Patrick Gelß (CoRR, 2023)

Koopman-Based Spectral Clustering of Directed and Time-Evolving Graphs. - Stefan Klus; Natasa Djurdjevac Conrad (J. Nonlinear Sci., 2023)

Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning. - Christof Schütte; Stefan Klus; Carsten Hartmann (Acta Numer., 2023)

Data-driven approximation of Koopman operators and generators: Convergence rates and error bounds. - Liam Llamazares-Elias; Samir Llamazares-Elías; Jonas Latz; Stefan Klus (CoRR, 2024)

Clustering Time-Evolving Networks Using the Dynamic Graph Laplacian. - Maia Trower; Natasa Djurdjevac Conrad; Stefan Klus (CoRR, 2024)

Learning dynamical systems from data: Gradient-based dictionary optimization. - Mohammad Tabish; Neil K. Chada; Stefan Klus (CoRR, 2024)

Clustering Time-Snapshots of Temporal Networks: From Synthetic Data to Real-World Applications. - Filip Blaskovic; Tim O. F. Conrad; Stefan Klus; Natasa Djurdjevac Conrad (CoRR, 2024)

Data-driven system identification using quadratic embeddings of nonlinear dynamics. - Stefan Klus; Joel-Pascal N'Konzi (CoRR, 2025)