CEE Seminar: Reduced-Order Modeling of High-Dimensional Systems Using Scientific Machine Learning

"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."
Join Zoom Meeting
https://duke.zoom.us/j/93940316282?pwd=OTJBQ25DdEtqMzJWK2U0ak1kRytadz09
Meeting ID: 939 4031 6282
Passcode: 019304