Events Calendar

AME Seminar

Speaker: Hannah Lu, MIT

Talk Title: Physics-Aware Data-Driven Modeling and Uncertainty Quantification for Large-Scale Environmental Problems

Abstract: Data-driven modeling of complex systems is a rapidly evolving field facilitated by the concurrent rise of data science. To alleviate the prohibitively expensive computational costs of repeated full-model simulations in uncertainty quantification, data-driven modeling is often used to describe the behaviors of the complex system by predicting the quantities of interest directly. In this talk, I will present my contributions to this field with an emphasis on (1) improving model performance by using physics-aware machine learning techniques, (2) quantifying uncertainties in the system’s response, and (3) inferring the key parameters of the physics-based models from measured data. Examples of applications will be focused on large-scale geological carbon sequestration—an important strategy for reducing greenhouse gas emissions to the atmosphere and mitigating climate change. The objective is to develop a convenient computing toolbox to provide more accurate scientific information at cheaper computational costs for better environmental management and decision-making.

Biography: Hannah Lu is a postdoc associate at MIT, affiliated with the Department of Aeronautics and Astronautics, Department of Civil Environmental Engineering, Earth Resources Laboratory and Laboratory for Information and Decision Systems. She obtained her Ph.D. from Energy Science and Engineering at Stanford Doerr School of Sustainability. Her research interests lie in the field of scientific computing, reduced order modeling, uncertainty quantification and machine learning in applications of environmental fluid mechanics. She received EDGE Doctoral Fellowship, Frank G. Miller Fellowship Award and Henry J. Ramey, Jr. Fellowship Award from Stanford University; Student Travel Award from SIAM Conference on UQ; NSF Fellowship from MMLDT-CSET Conference; Travel Grant from NSF-funded HydroML Symposium; and a first-place USNCCM17 Best Presentation Award in postdoc category.

Host: AME Department

More Info: https://ame.usc.edu/seminars/

Webcast: https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09

Location: James H. Zumberge Hall Of Science (ZHS) - 252

WebCast Link: https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09

Audiences: Everyone Is Invited

Contact: Tessa Yao

Event Link: https://ame.usc.edu/seminars/