274 citations · 1.8k across the 48 of their papers we have counts for
6 papers · 1 filter
A kernel-based approach to molecular conformation analysis
Stefan Klus, Andreas Bittracher, Ingmar Schuster +1
We present a novel machine learning approach to understanding conformation dynamics of biomolecules. The approach combines kernel-based techniques that are popular in the machine l…
Multidimensional approximation of nonlinear dynamical systems
Patrick Gelß, Stefan Klus, Jens Eisert +1
A key task in the field of modeling and analyzing nonlinear dynamical systems is the recovery of unknown governing equations from measurement data only. There is a wide range of ap…
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces
Mattes Mollenhauer, Ingmar Schuster, Stefan Klus +1
Reproducing kernel Hilbert spaces (RKHSs) play an important role in many statistics and machine learning applications ranging from support vector machines to Gaussian processes and…
Feedback control of nonlinear PDEs using data-efficient reduced order models based on the Koopman operator
Sebastian Peitz, Stefan Klus
In the development of model predictive controllers for PDE-constrained problems, the use of reduced order models is essential to enable real-time applicability. Besides local linea…
Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions
Stefan Klus, Sebastian Peitz, Ingmar Schuster
Kernel transfer operators, which can be regarded as approximations of transfer operators such as the Perron-Frobenius or Koopman operator in reproducing kernel Hilbert spaces, are…
Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives
Sören Hanke, Sebastian Peitz, Oliver Wallscheid +3
Predictive control of power electronic systems always requires a suitable model of the plant. Using typical physics-based white box models, a trade-off between model complexity (i.…