Title: Randomized local model order reduction

Applications that require multiple simulation requests or a real-time simulation response are ubiquitous in science and engineering. However, using standard methods such as finite elements is often prohibitive for such tasks. Model order reduction approaches, in which the equation that models the considered phenomena is (approximately) solved in a carefully chosen subspace of the high-dimensional discretization space, have been developed to tackle such situations.

In this talk we show how to use local reduced models within domain decomposition methods to compute fast approximations for large-scale applications. For the efficient construct of the local reduced models we employ randomized methods used in data science, compressed sensing, and deep learning.

Kathrin Smetana is an Assistant Professor at the Department of Applied Mathematics at theUniversity of Twente. Prior to that appointment she worked as a postdoctoral associate in the group of Professor Mario Ohlberger in the Faculty ofMathematics and Computer Science at the University of Münster, Germany and in the group of Professor Anthony T. Patera in the Department of Mechanical Engineering at the Massachusetts Institute of Technology, United States. Kathrin Smetana holds a PhD in Mathematics from the University of Münster. Recently she received the Professor De Winter Award for her publication on randomized multiscale and domain decomposition methods. The main foci of her research are model reduction, domain decomposition, and multiscale methods and randomized algorithms for numerical simulations.