Numerical Analysis and Scientific Computing Seminar | Romit Maulik, Reduced-order modeling of high-dimensional systems using scientific machine learning

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.