Published: Nov. 9, 2020

Alireza DoostanAlireza Doostan
Associate Professor, Smead Aerospace
Friday, Nov. 13 | 12:30 P.M. | Zoom Webinar - Registration Required

Abstract: The increasing power of computing platforms and the recent advances in data science techniques have fostered the development of data-driven computational models of engineering systems with considerably improved prediction accuracies. An important feature of these modeling approaches is the reliance on data to develop reduced-order models of physical phenomena involved and/or the characterization of the uncertainty associated with the models or their parameters.  In the latter case, the quantification of the impact of such uncertainty on the quantities of interest is key to assess the validity of a given model and, potentially, its refinement. However, for complex engineering systems, such as those featuring multi-physics and multi-scale phenomena, data is often high-dimensional and the simulation models are computationally expensive. These, in turn, pose significant challenges to standard data-driven approaches. 

I will start this talk with a brief discussion on the challenges associated with uncertainty quantification (UQ) and data management of complex systems and a high-level introduction to recent work performed by my research group to tackle these challenges. I will then focus on model reduction approaches for efficient UQ and data storage. While seemingly different, I will explain how these two problems can be tackled with similar computational strategies. At the core of these techniques is a systematic use of models with different levels of fidelity, e.g., coarse vs. fine discretization of the same problem, that enables the identification of a lower-dimensional, yet accurate, description of the quantities of interest or data. During the talk, I will present application examples to highlight the efficiency of these multi-fidelity model reduction approaches and their wide applicability to a broad range of problems.

Bio: Alireza Doostan is an H. Joseph Smead Faculty Fellow and Associate Professor of Aerospace Engineering Sciences Department at the University of Colorado Boulder. He is also the director of the Center for Aerospace Structures (CAS) and an affiliated faculty of the Applied Mathematics Department. Prior to his appointment at CU Boulder in 2010, he was an Engineering Research Associate in the Center for Turbulence Research at Stanford University. Alireza received his PhD in Structural Engineering and M.A. in Applied Mathematics and Statistics from the Johns Hopkins University both in 2007. He is a recipient of a DOE (ASCR) and an NSF (Engineering Design) Early Career awards, as well as multiple teaching awards from CU Boulder and AIAA. His research interests include: Uncertainty quantification, data-driven modeling, optimization under uncertainty, and computational stochastic mechanics.