For Zoom Link please contact ddelreyfernandez@uwaterloo.ca

## Speaker

Romit Maulik, Computational Scientist in the Mathematics and Computer Division at Argonne National Laboratory (Research Assistant Professor in the Department of Applied Mathematics at the Illinois Institute of Technology)

## Title

Reduced-order modeling of high-dimensional systems using scientific machine learning

## Abstract

In this talk, I will present recent research that builds fast and accurate reduced-order models (ROMs) for various high-dimensional systems. These systems may be steady-state, where the ROM is tasked with making predictions given varying parametric inputs, or they may be dynamic where the ROM must make accurate forecasts in time, given parameters and/or varying initial and boundary conditions. In both endeavors, we will outline the development of scientific machine learning strategies, based on deep learning-based compression and forecasting, to dramatically improve accuracy and time-to-solution for extended computational campaigns. Furthermore, in addition to canonical experiments, our algorithms will be demonstrated for several real-world applications of strategic importance. Some examples are building ROMs for geophysical forecasting from ship and satellite observation data and wind-turbine wake predictions from meteorological and LIDAR measurements.