Learning dynamical laws from data for complex processes

Speaker: Stefan Klus, FU-Berlin

A key task in the field of modeling and analyzing complex processes is the recovery of unknown governing dynamical laws from measurement data only. There is a wide range of application areas for this important instance of system identification, ranging from industrial engineering, molecular or cellular dynamics to stock market processes. In order to find appropriate representations of underlying dynamical systems, various data-driven methods have been proposed by different communities. However, if the given data sets are high-dimensional, then these methods typically suffer from the curse of dimensionality. We discuss some of the machine learning approaches used and discuss how to significantly reduce the computational costs and storage consumption. The approaches will be illustrated with the aid of several applications.