Thursday, April 7, 2022
5 p.m., on Zoom
In this talk, we will investigate various approaches to modeling dynamical systems from data. We will consider both frequency-domain and time-domain measurements of a dynamical system using systems theoretical concepts. In the former, data will correspond to the samples of a transfer function and we will show how to use these samples to learn reduced-order dynamics via rational interpolation and rational least-squares fitting. We will also extend these ideas to present a data-driven formulation for balanced truncation. In the case of time-domain data, we will assume access to (a subset of) state snapshots and use a least-squares minimization to learn the dynamics.
