Talk
Learning dynamical systems from data
- Felix Dietrich (Technical University of Munich)
Abstract
Dynamic processes have been modeled successfully for hundreds of years, often using ordinary or partial differential equations. Using data-driven methods, these processes can now also be inferred directly from measurements. In this talk, I will provide an overview of our work in this direction. I will discuss learning differential equations on reduced spaces, how to utilize numerical integration schemes to train neural networks for stochastic dynamics, and close with an alternative view on system identification with the Koopman operator framework.
