4 papers
Invariance of Gaussian RKHSs under Koopman operators of stochastic differential equations with constant matrix coefficients
Friedrich Philipp, Manuel Schaller, Karl Worthmann +2
We consider the Koopman operator semigroup associated with stochastic differential equations of the form with constant matrices and…
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…
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…