Knots, Möbius bands, and inscribed rectangles
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.
Serkan Gugercin is a professor in the Department of Mathematics and in the Division of Computational Modeling and Data Analytics (CMDA) in the College of Science at Virginia Tech. He is also an Affiliated Faculty in the Department of Mechanical Engineering. His research is in the areas of Model Reduction, Dynamical Systems, Numerical Analysis, and Scientific Computing.