Overview

Contact

Stefan Klus
Department of Mathematics and Computer Science
Arnimallee 9
14195 Berlin

+49 (0)30 838 72030
stefan.klusnull@nullfu-berlin.de

Not updated anymore. See my University of Surrey profile page instead.

Research interests
  • Data-driven model reduction and transfer operator approximation
  • Reproducing kernel Hilbert spaces and statistical learning theory
  • Molecular dynamics
  • Low-rank tensor decompositions
  • Multi-rate integrators
  • Numerical solution of differential-algebraic equations
  • Real-time simulation and high-performance computing
  • Graph theory
  • Combinatorial optimization
Publications
  • K. Melnyk, S. Klus, G. Montavon, and T. Conrad. GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis. Preprint, 2020. (arXiv)
  • A. Bittracher, SK, B. Hamzi, P. Koltai, and C. Schütte. A kernel-based method for coarse graining complex dynamical systems. Journal of Nonlinear Science, 2020. (arXiv)
  • I. Schuster, M. Mollenhauer, SK, and K. Muandet. Kernel Conditional Density Operators. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, 108:993-1004, 2020. (arXiv) (PMLR)
  • M. Mollenhauer, I. Schuster, SK, and C. Schütte. Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces. In O. Junge et al.  Advances in Dynamics, Optimization and Computation. Springer, 2020. (arXiv) (Springer)
  • M. Dellnitz, B. Gebken, R. Gerlach, and SK. On the Equivariance Properties of Self-adjoint Matrices. Dynamical Systems, 35(2):197-215, 2020.  (arxiv) (DS)
  • SK, F. Nüske, and B. Hamzi. Kernel-based approximation of the Koopman generator and Schrödinger operator. Entropy, 22(7):722, 2020.  (arXiv) (entropy)
  • M. Mollenhauer, S. Klus, C. Schütte, and P. Koltai. Kernel autocovariance operators of stationary processes: Estimation and convergence. Preprint, 2020. (arXiv)
  • F. Nüske, P. Gelß, SK, and C. Clementi. Tensor-based computation of metastable and coherent sets. Preprint, 2020. (arXiv)
  • SK, F. Nüske, S. Peitz, J.-H. Niemann, C. Clementi, and C. Schütte. Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. Physica D: Nonlinear Phenomena, 406:132416, 2020. (arXiv) (Physica D)
  • S. Peitz and SK. Feedback control of nonlinear PDEs using data-efficient reduced order models based on the Koopman operator. In A. Mauroy, I. Mezic, and Y. Susuki. The Koopman Operator in Systems and Control: Concepts, Methodologies and Applications. Springer, 2020. (arXiv) (Springer)
  • SK, I. Schuster, and K. Muandet. Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces. Journal of Nonlinear Science, 30(1):283-315, 2020. (arXiv) (JNLS)
  • SK, B. E. Husic, M. Mollenhauer, and F. Noé. Kernel methods for detecting coherent structures in dynamical data. Chaos, 2019. (arXiv) (Chaos)
  • W. Zhang, SK, T. Conrad, and C. Schütte. Learning Chemical Reaction Networks from Trajectory Data. SIAM Journal on Applied Dynamical Systems , 18(4):2000–2046, 2019. (arXiv) (SIADS)
  • SK and P. Gelß. Tensor-based algorithms for image classification. Algorithms, 12(11):240, 2019. (arXiv) (Algorithms)
  • T. Sahai, A. Ziessler, SK, and M. Dellnitz. Continuous Relaxations for the Traveling Salesman Problem. Nonlinear Dynamics, 97(4): 2003-2022, 2019. (arXiv) (Nonlinear Dynamics)
  • S. Peitz and SK. Koopman operator-based model reduction for switched-system control of PDEs. Automatica, 106: 184-191, 2019. (arXiv) (automatica)
  • P. Gelß, SK, J. Eisert, and C. Schütte. Multidimensional approximation of nonlinear dynamical systems. Journal of Computational and Nonlinear Dynamics, 14(6):061006, 2019. (arXiv)  (CND)
  • SK, A. Bittracher, I. Schuster, and C. Schütte. A kernel-based approach to molecular conformation analysis. The Journal of Chemical Physics,  149(24):244109, 2018. (arXiv) (J. Chem. Phys.)
  • SK, P. Gelß, S. Peitz, and C. Schütte. Tensor-based dynamic mode decomposition. Nonlinearity, 2018. (arXiv) (nonlinearity)
  • R. Banisch, Z. Trstanova, A. Bittracher, SK, and P. Koltai. Diffusion maps tailored to arbitrary non-degenerate Itô processes. Applied and Computational Harmonic Analysis, 2018. (arXiv) (ACHA)
  • SK, S. Peitz, and I. Schuster. Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions. Preprint, 2018. (arXiv)
  • SK, F. Nüske, P. Koltai, H. Wu, I. Kevrekidis, C. Schütte, and F. Noé. Data-driven model reduction and transfer operator approximation. Journal of Nonlinear Science, 28(3):985-1010, 2018. (arXiv) (JNLS)
  • S. Hanke, S. Peitz, O. Wallscheid, SK, J. Böcker, and M. Dellnitz. Koopman Operator Based Finite-Set Model Predictive Control for Electrical Drives. Preprint, 2018. (arXiv)
  • A. Bittracher, P. Koltai, SK, R. Banisch, M. Dellnitz, and C. Schütte. Transition manifolds of complex metastable systems: Theory and data-driven computation of effective dynamics. Journal of Nonlinear Science, 28(2):471-512, 2018. (arXiv) (JNLS)
  • SK and T. Sahai. A spectral assignment approach for the graph isomorphism problem. Information and Inference: a Journal of the IMA, 7(4):689-706, 2018. (arXiv) (IMAIAI)
  • S. Peleš and SK. Sparse automatic differentiation for large-scale computations using abstract elementary algebra. International Journal of Numerical Analysis and Modeling, 14(6):916-934, 2017. (arXiv) (IJNAM)
  • P. Gelß, SK, S. Matera, and C. Schütte. Nearest-Neighbor Interaction Systems in the Tensor Train Format. Journal of Computational Physics, 341:140-162, 2017. (arXiv) (J. Comp. Phys.)
  • H. Wu, F. Nüske, F. Paul, SK, P. Koltai, and F. Noé. Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations. The Journal of Chemical Physics, 146(15):154104, 2017. (arXiv) (J. Chem. Phys.)
  • M. Dellnitz and SK. Sensing and Control in Symmetric Networks. Dynamical Systems, 32(1):61-79, 2017. (arXiv) (DS)
  • M. Dellnitz, SK, and A. Ziessler. A Set-Oriented Numerical Approach for Dynamical Systems with Parameter Uncertainty. SIAM Journal on Applied Dynamical Systems, 16(1):120-138, 2016. (arXiv) (SIADS) (DSWEB)
  • SK and C. Schütte. Towards tensor-based methods for the numerical approximation of the Perron-Frobenius and Koopman operator. Journal of Computational Dynamics, 3(2):139-161, 2016. (arXiv) (JCD)
  • SK, P. Koltai, and C. Schütte. On the numerical approximation of the Perron-Frobenius and Koopman operator. Journal of Computational Dynamics, 3(1):51-79, 2016. (arXiv) (JCD)
  • SK. Signal-flow based Runge-Kutta methods for the simulation of complex networks. ArXiv e-prints, 2015.
  • SK. Signal-Flow Based Circuit Simulation. PhD thesis, Institute for Industrial Mathematics, Paderborn University, 2011.
  • SK, T. Sahai, C. Liu, and M. Dellnitz. An efficient algorithm for the parallel solution of high-dimensional differential equations. Journal of Computational and Applied Mathematics, 235(9):3053-3062, 2011. (arXiv) (JCAM)
  • C. Lakemeyer, SK, K. Anger, H. Hörmann, and V. Schöppner. Temperature modelling of the melt for tempered screws in single screw extrusion by giving constant heat fluxes. In Proceedings of the Polymer Processing Society, Istanbul, Turkey, 2010.
  • H. Potente, V. Schöppner, SK, C. Anger, and H. Hörmann. Temperature modelling of the melt for tempered screws in single screw extrusion. In Proceedings of the 25th Annual Meeting of the Polymer Processing Society, Goa, India, 2009.
  • H. Potente, M. Kurte-Jardin, K. Timmermann, and SK. Temperature development of wall-slipping melts. In Proceedings of the 22nd Annual Meeting of the Polymer Processing Society, Yamagata, Japan, 2006.
  • H. Potente, M. Kurte-Jardin, SK, and K. Timmermann. Two dimensional description of pressure-throughput behaviour of Newtonian materials considering wall slippage effects. International Polymer Processing, 20(3):312-321, 2005.