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13th Elsevier Distinguished Lecture in Mechanics

Thursday, March 4, 2021

Quantum Information and Deep Learning for Turbulent Combustion Modeling & Simulation 
Sponsored by Elsevier and the Journal: Mechanics Research Communications

Peyman Givi pic
Presented by:
Peyman Givi, Ph.D. 
Distinguished  Professor Swanson School, Engineering University of Pittsburgh

Logistics:
April 6, 2021
11:00 AM-12:30 PM
https://pit.tzoom.us/j/93485294583/

This lecture is focused on recent work in which use is made of modern developments in Quantum Computing (QC), and Deep Learning (DL) & Machine Learning (ML) to tackle some of the most challenging problems in turbulent combustion. The computational approach is via a stochastic model termed the Filtered Density Function (FDF).  This  model, originally developed by this lecturer, provides one of the most systematic means of describing the unsteady evolution of reactive  turbulence.  It is demonstrated that, if devised intelligently, DL/ML can aid in developments of high fidelity FDF closures, and QC provides a significant speed-up over classical FDF simulators.

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