5 papers · 1 filter
Koopman for stochastic dynamics: error bounds for kernel extended dynamic mode decomposition
Maximiliano Hertel, Friedrich M. Philipp, Manuel Schaller +1
We prove -error bounds for kernel extended dynamic mode decomposition (kEDMD) approximants of the Koopman operator for stochastic dynamical systems. To this end, we estab…
Group-Convolutional Extended Dynamic Mode Decomposition
Hans Harder, Feliks Nüske, Friedrich M. Philipp +3
This paper explores the integration of symmetries into the Koopman-operator framework for the analysis and efficient learning of equivariant dynamical systems using a group-convolu…
-error bounds for approximations of the Koopman operator by kernel extended dynamic mode decomposition
Frederik Köhne, Friedrich M. Philipp, Manuel Schaller +2
Extended dynamic mode decomposition (EDMD) is a well-established method to generate a data-driven approximation of the Koopman operator for analysis and prediction of nonlinear dyn…
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…