Dr. Elizabeth Qian
Abstract:
Machine learning (ML) methods have garnered significant interest as potential approaches for constructing surrogate models of complex engineering systems for which traditional simulation is computationally expensive. However, in many scientific and engineering applications, training data are scarce because of the high cost of generating data from traditional high-fidelity simulations. ML models trained on limited data often yield unreliable predictions outside the regime represented by the training data.
This talk presents multifidelity training approaches for ML that exploit scientific settings in which data of varying fidelities and computational costs are available. For example, high-fidelity data may be generated by an expensive, fully resolved physics simulation, whereas lower-fidelity data may be obtained from a less computationally expensive model based on simplifying assumptions. Two recent approaches developed in the speaker’s research group for multifidelity training are highlighted: one based on linear regression and another based on nonlinear autoregressive Gaussian process regression. The performance of these approaches is demonstrated on problems arising in the modeling of reacting flows.
Speaker’s Bio:
Dr. Elizabeth Qian holds a joint appointment at the Georgia Institute of Technology as an Assistant Professor in the School of Aerospace Engineering and the School of Computational Science and Engineering. Her interdisciplinary research focuses on developing computational methods that enable engineering design and decision-making for complex systems. Her research specialties include the development of efficient surrogate models through model reduction and scientific ML, as well as multifidelity approaches for accelerating expensive computations in uncertainty quantification, optimization, and control.
Dr. Qian previously held a postdoctoral appointment as a von Kármán Instructor in the Department of Computing + Mathematical Sciences at the California Institute of Technology. She earned her Doctor of Philosophy, Master of Science, and Bachelor of Science degrees from the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology. Her awards and honors include a Caltech-wide teaching award selected by the undergraduate student body, the 2020 Society for Industrial and Applied Mathematics Student Paper Prize, a Fannie and John Hertz Foundation Fellowship, a National Science Foundation Graduate Research Fellowship, and a United States Fulbright Student grant. She currently holds a visiting faculty appointment as a Hans Fischer Fellow at the Technical University of Munich