Tuesday, April 27, 2021 from 7:00 PM to 8:30 PM PDT
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Rachel Horzewski 
Pacific Northwest AIAA 

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Tech Talk #2

Tuesday, April 27, 2021 via Zoom 
7:00 - 8:30 pm PDT

Machine Learning for Fluid Mechanics 
Steven Brunton

Speaker Bio

Steven L. Brunton is an Associate Professor of Mechanical Engineering at the University of Washington.  He is Adjunct Associate Professor of Applied Mathematics and a Data Science Fellow at the eScience Institute.  Steve received the B.S. in mathematics from Caltech (2006) and the Ph.D. in mechanical and aerospace engineering from Princeton (2012).  His research combines machine learning with dynamical systems to model and control systems in fluid dynamics, biolocomotion, optics, energy systems, and manufacturing.  He has co-authored three textbooks, received the Army and Air Force Young Investigator Program awards and the Presidential Early Career Award for Scientists and Engineers (PECASE).

Talk Abstract

Many tasks in fluid mechanics, such as design optimization and control, are challenging because fluids are nonlinear and exhibit a large range of scales in both space and time. This range of scales necessitates exceedingly high-dimensional measurements and computational discretization to resolve all relevant features, resulting in vast data sets and time-intensive computations. Indeed, fluid dynamics is one of the original big data fields, and many high-performance computing architectures, experimental measurement techniques, and advanced data processing and visualization algorithms were driven by decades of research in fluid mechanics. Machine learning constitutes a growing set of powerful techniques to extract patterns and build models from this data, complementing the existing theoretical, numerical, and experimental efforts in fluid mechanics. In this talk, we will explore current goals and opportunities for machine learning in fluid mechanics, and we will highlight a number of recent technical advances. Because fluid dynamics is central to transportation, health, and defense systems, we will emphasize the importance of machine learning solutions that are interpretable, explainable, generalizable, and that respect known physics.