Seminar: Nonlinear Dimensionality Reduction: From Turbulent Fluid Flows to Computational Finance

The past several decades have seen an exponential growth of computer processing speed and memory capacity.  The massive, complex simulations that run on supercomputers allow exploration of fields for which physical experiments are too impractical, hazardous, and/or costly.  Accurate and efficient high-fidelity simulations are critical to many energy, defense, and health applications, e.g., global climate simulations, optimal design of wind systems for power generation, combustion simulations aimed at increasing fuel efficiency and reducing carbon emissions, simulations of heart fibrillation, and many others. Unfortunately, even with the aid of massively parallel next-generation computers, high-fidelity simulations are still too expensive for real-time and multi-query applications such as uncertainty quantification, design, optimization, and control. For this reason, interest in model order reduction continues to grow. In this talk I will summarize recent advances in nonlinear model reduction for high-Reynolds-number fluid flows, structural contact problems, and computational finance.

About the Speaker

Dr. Balajewicz is an Assistant Professor in the Department of
Aerospace Engineering at The University of Illinois at Urbana-Champaign, and Adjunct Assistant Professor in the Department of Mechanical and Material Science at Duke University. He was a Postdoctoral Research Fellow in the Department of Aeronautics and Astronautics at Stanford University, and received his Ph.D. from Duke University. His research interests include nonlinear model reduction, turbulence, system identification, and machine learning.

Hosted by Professor Jack McNamara