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"Nonlinear Surrogate Models for Computational Fluid Dynamics"

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Friday, March 18, 2022, 1 pm– 2 pm

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Abstract: 

High-fidelity models are expensive for numerical simulations, especially in multi-query scenarios, thus reduced-order modeling has been introduced to provide computationally cheap surrogates. Such reduced-order models are constructed offline based on a collection of snapshot data that are of low dimensions and can be efficiently simulated at an online stage. Although the reduced-order modeling has achieved many successes, its efficacy could degrade when the problem of interest has a slowly decaying Kolmogorov n-width or a high-dimensional parameter space. In this talk, we consider complex flows and introduce data-driven closure methods and deep-learning based dimensionality reduction approaches to overcome these challenges and develop improved nonlinear surrogate models.

Bio:

Dr. Zhu Wang is an associate professor in the Department of Mathematics at University of South Carolina (USC) since 2018. Before he joined USC in 2014, he was an industrial postdoc of the IMA at University of Minnesota, Twin Cities. He obtained his Ph.D. from the Department of Mathematics at Virginia Tech in Spring, 2012, under the supervision of Dr. Traian Iliescu.
In 2020, he was invited to participate as a Research Fellow at the Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University. In 2019, he was selected as one of USC Breakthrough Stars in 2019.
His research interests include scientific computing, data science, numerical analysis, reduced-order modeling, climate modeling, and mathematical theory in machine learning and inverse problems.

 

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