Speaker
Friedrich M. Philipp
Description
Extended dynamic mode decomposition (EDMD), embedded in the Koopman framework, is a widely-applied technique for prediction and control of dynamical control systems. In this talk, we discuss recent uniform error bounds for kernel-based EDMD. Leveraging the interpolation property of regression problems in Reproducing Kernel Hilbert Spaces, we deduce uniform error bounds for kernel-based EDMD. A particular feature is an explicit dependence of the error bound on the distance to the data set. We show that this property is a crucial ingredient for data-driven stability analysis, feedback control and predictive control with guarantees.
