[IGPP Everyone] Today - Last Seminar for the Quarter - SPACE PHYSICS and HERMES SCIENCE CENTER SEMINAR - Fall Quarter - Friday December 4th, 2020 - 3:30pm - UCLA Zoom

[Marjorie Sowmendran]

Don't miss the last Seminar for the Quarter 

R E M I N D E R - T O D A Y 


SPACE PHYSICS and HERMES SCIENCE CENTER SEMINAR 



ZOOM LINK PROVIDED BELOW 





https://ucla.zoom.us/j/93382900867?pwd=TlVQRmZyVEJFa0FCZWlRMU0yREtjUT09 







Date/Time: December 4, 2020/ 03:30 PM Pacific Time (US and Canada) 




SPACE PHYSICS and HERMES SCIENCE CENTER SEMINAR 







DEPARTMENT OF EARTH, PLANETARY, AND SPACE SCIENCES 

DEPARTMENT OF ATMOSPHERIC AND OCEANIC SCIENCES 

UNIVERSITY OF CALIFORNIA, LOS ANGELES 





Data-driven discovery of reduced plasma physics models from fully-kinetic simulations 

Prof. Paulo Alves, UCLA 

At the core of some of the most important problems in plasma physics — from controlled nuclear fusion to the acceleration of cosmic rays — is the challenge to describe nonlinear, multi-scale plasma dynamics. The development of reduced plasma models that balance between accuracy and complexity is critical to advancing theoretical comprehension and enabling holistic computational descriptions of these problems. In this talk I will discuss how techniques from statistical and machine learning are offering new ways of inferring reduced plasma physics models from the increasingly abundant data of plasma dynamics produced by experiments, observations and simulations. In particular, I will focus on how sparse regression techniques can be used to infer interpretable plasma physics models (in the form of nonlinear partial differential equations) directly from the data of fully-kinetic particle-in-cell (PIC) simulations. I will demonstrate the potential of this approach by recovering the fundamental hierarchy of plasma physics models — from the Vlasov equation to magnetohydrodynamics — based solely on data of complex plasma dynamics captured by first-principles PIC simulations. I will conclude with some perspectives on how this data-driven methodology can offer a promising new route to accelerate the development of reduced theoretical models of complex nonlinear plasma phenomena and to design computationally efficient algorithms for multi-scale plasma simulations. 



Friday, December 4, 2020 

3:30 - 5:00 PM 




In-Charge: Marco Velli 







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