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Machine Learning for Turbulence

Event Type: 
Date and Time: 
Friday, May 7, 2021 - 16:30
Location: 
Zoom
Event Sponsor: 
Parviz Moin, Director of Center for Turbulence Research
Speaker(s): 
Dr. Daniel Livescu

This talk summarizes part of the work performed during our 3-year Laboratory Directed Research and Development - Directed Research (LDRD-DR) project, titled MachinE Learning for Turbulence (MELT).  Started in October 2018, the project partially covered ~10 staff members, 7 postdocs, several more summer students, and addressed a diverse set of topics related to turbulence and applications in climate and astrophysics.  Today, in order to highlight the exploratory aspect of such projects, I will survey some of our results on a) neural network (NN) models of scalar turbulence, b) learning Lagrangian dynamics, c) Mori-Swanzig structure of turbulence and, finally, d) unsupervised identification of dynamical regimes.

Link to video file:
Machine Learning for Turbulence

Bio: 
Dr. Daniel Livescu has been a scientist at Los Alamos National Laboratory since he received his Ph.D. in 2001 and, currently, is leading the fluid dynamics team within the CCS Division and is the PI for OE/NNSA Office of Experimental Sciences program on DNS. His research interests are in the general areas of theoretical and computational fluid mechanics, with emphasis on turbulence and turbulent mixing simulation, theory, and modeling. Dr. Livescu is a Fellow ASME, Associate Fellow AIAA, and the recipient of the 2017 (inaugural) Frank Harlow Distinguished Mentor Award. He also serves as Associate Editor for AIAA Journal, ASME Journal of Fluids Engineering, and has recently joined the Editorial Committee of the Annual Review of Fluid Mechanics.