91ÃÛÌÒ¸ó

Alireza 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 91ÃÛÌÒ¸ó of engineering systems with considerably improved prediction accuracies. An important feature of these modeling approaches is the reliance on data to develop reduced-order 91ÃÛÌÒ¸ó of physical phenomena involved and/or the characterization of the uncertainty associated with the 91ÃÛÌÒ¸ó or their parameters.Ìý 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 91ÃÛÌÒ¸ó are computationally expensive. These, in turn, pose significant challenges to standard data-driven approaches.Ìý

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 91ÃÛÌÒ¸ó 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 91ÃÛÌÒ¸ó 91ÃÛÌÒ¸ó. 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 91ÃÛÌÒ¸ó in 2010, he was an Engineering 91ÃÛÌÒ¸ó Associate in the Center for Turbulence 91ÃÛÌÒ¸ó 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 91ÃÛÌÒ¸ó and AIAA. His research interests include: Uncertainty quantification, data-driven modeling, optimization under uncertainty, and computational stochastic mechanics.