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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Seminar Date: Thursday, March 26, 2020
Seminar Time: 11:00 am
How to Attend:
To join the meeting on a computer or mobile phone: https://bluejeans.com/930867085/
Meeting ID: 930 867 085
Want to test your video connection? https://bluejeans.com/111
Talk Title: Extreme event quantification in intermittent dynamical systems
Talk Abstract: A wide range of dynamical systems encountered in nature and technology are characterized by the presence of intermittent events with strongly transient characteristics, such as in turbulent fluid flows, water waves, passive tracers, and numerous other engineering systems. Although extreme events typically occur infrequently, they usually have drastic consequences and are important to quantify for design optimization, uncertainty quantification, and reliability assessment. There is a practical need for quickly evaluating the probabilistic response, including extreme event statistics, for such systems that are undergoing transient and extreme responses, but unfortunately, the task is often too computational demanding to make analysis computational feasible. I present a decomposition based probabilistic approach that can accurately capture the probability distribution, many standard deviations away from the mean, at a fraction of the cost of Monte Carlo simulations, for a specific class of intermittent dynamical systems and a more general adaptive sampling based method to capture response statistics from a limited dataset for black-box like systems. These methods are demonstrated to examples ranging from large waves in the ocean to structural systems subjected to extreme forcing events. We also discuss the assimilation of passive scalar exhibiting fat-tails via unique filtering algorithms that allow us to capture model physics and the underlying turbulent spectra of the flow.
Bio: Mustafa Mohamad received his Bachelor’s degree in Engineering Mechanics with a minor in Mathematics in 2012 from the University of Illinois at Urbana-Champaign, graduating with highest honors as a Bronze tablet scholar. He obtained both his Master’s degree in 2015 and PhD in 2017 both from the Massachusetts Institute of Technology in Mechanical Engineering and Computation working on extreme events in stochastic dynamical systems. He is currently a postdoctoral associate at the Courant Institute of Mathematical Sciences at New York University studying data assimilation algorithms for passively advected particles in fluid flows.
Host: Elizabeth Cherry
