274 citations · 1k across the 43 of their papers we have counts for
8 papers · 1 filter
Variance representations and convergence rates for data-driven approximations of Koopman operators
Friedrich M. Philipp, Manuel Schaller, Septimus Boshoff +3
We rigorously derive novel error bounds for extended dynamic mode decomposition (EDMD) to approximate the Koopman operator for discrete- and continuous time (stochastic) systems; b…
Error analysis of kernel EDMD for prediction and control in the Koopman framework
Friedrich Philipp, Manuel Schaller, Karl Worthmann +2
Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the Koopman operator for deterministic and stochastic (control) systems. This operator is…
Equivariance and partial observations in Koopman operator theory for partial differential equations
Sebastian Peitz, Hans Harder, Feliks Nüske +3
The Koopman operator has become an essential tool for data-driven analysis, prediction and control of complex systems. The main reason is the enormous potential of identifying line…
Error bounds for kernel-based approximations of the Koopman operator
Friedrich Philipp, Manuel Schaller, Karl Worthmann +2
We consider the data-driven approximation of the Koopman operator for stochastic differential equations on reproducing kernel Hilbert spaces (RKHS). Our focus is on the estimation…
Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories
Samuel E. Otto, Sebastian Peitz, Clarence W. Rowley
Data-driven models for nonlinear dynamical systems based on approximating the underlying Koopman operator or generator have proven to be successful tools for forecasting, feature l…
Efficient time stepping for numerical integration using reinforcement learning
Michael Dellnitz, Eyke Hüllermeier, Marvin Lücke +4
Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamic…
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
Stefan Klus, Feliks Nüske, Sebastian Peitz +3
We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode deco…
Koopman operator-based model reduction for switched-system control of PDEs
Sebastian Peitz, Stefan Klus
We present a new framework for optimal and feedback control of PDEs using Koopman operator-based reduced order models (K-ROMs). The Koopman operator is a linear but infinite-dimens…