You're invited to attend

"Interpretable Reduced-Complexity Models for Engineering Applications"

by

Katherine Asztalos

Postdoctoral Scholar | Transportation and Power Systems Division | Argonne National Laboratory

Thursday, November 30
3:30 - 4:30 p.m.
Clary Theater, Student Success Center
 

About the Seminar: 
Developing reduced-complexity models has become increasingly popular within the field of fluid mechanics. They can be utilized to represent complex systems in an optimal low-dimensional space, develop predictive models with reduced computational expense, and further uncover underlying dynamical information. These models may be developed from either a physics-based approach, in which the governing equations are utilized, or from data-driven methods, in which an array of training data for the system might be available. This talk discusses the use of data-driven and physics-based modeling methodologies to capture and predict complex fluid dynamical behavior for problems relevant to engineering applications such as unsteady aerodynamics and internal multiphase flows.  

About the Speaker:
Katherine Asztalos is a current postdoctoral scholar in the Transportation and Power Systems Division at Argonne National Laboratory. Her research interests lie primarily in reduced-order modeling and optimization of dynamical systems in fluid mechanics, computational fluid dynamics, internal multiphase flows, and flow control for unsteady aerodynamic applications. Dr. Asztalos received her PhD from the Illinois Institute of Technology in Mechanical and Aerospace Engineering in 2021 for modeling the aerodynamic response to impulsive active flow control. She also holds a Master of Engineering in Mechanical and Aerospace Engineering and a Bachelor of Science in Aerospace Engineering from the Illinois Institute of Technology.