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5.12.16 Duraisamy

Topic: Big Data Analytics and Machine Intelligence Seminar #13: Creating an Ecosystem for Data-Enabled Modeling of Multi-Scale Physical Systems 

Speaker: Dr. Karthik Duraisamy, University of Michigan

When: May 12, 2016

Time: 9:30 – 10:30am

Where: NASA LaRC Bldg 1268; Rm 2120

Abstract: The pursuit of accurate predictive models is a central issue and pacing item in many scientific and engineering disciplines. With the recent growth in computational power and measurement resolution, there is an unprecedented opportunity to use data from fine-scale simulations, as well as critical experiments, to inform, and in some cases even define predictive models. The task of deriving models from data requires the coordination of experimental design, data decomposition, statistical inference, machine learning, physical modeling and domain expertise.

In this talk, one such paradigm is introduced with the goal of comprehensively harnessing data to aid the creation of improved models for computational physics applications. By directly addressing the connection between physical data and model discrepancies, field inversion materially enhances the value of computational and experimental data for model improvement. The resulting information can be used by the modeler as a guiding tool to design more accurate model forms, or serve as input to machine learning algorithms to directly replace deficient modeling terms. As a demonstrative example, modeling of turbulent flows will be presented.

The final part of the talk will provide a brief overview of a hardware/software ecosystem that is being developed at the University of Michigan to enable large-scale data-driven model development for computational physics applications.

Bio: Dr. Duraisamy obtained a doctorate in aerospace engineering and master’s degree in applied mathematics from the University of Maryland, College Park. Prior to his appointment in 2013 at the University of Michigan, he spent time at Stanford University and the University of Glasgow. At the University of Michigan, he is the founding director of the Center for Data-driven Computational Physics, which involves 10 affiliated faculty members and is focused on deriving data-driven solutions to complex multi-physics problems in many fields. His other research interests are in turbulence modeling and simulations, numerical methods and reduced-order modeling.

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