BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Adaptive sparse grids to reduce noise in numerical simulations of 
 kinetic plasmas - Antoine Cerfon (Courant Institute of Mathematical Scienc
 es\, New York University)
DTSTART:20220425T090000Z
DTEND:20220425T100000Z
UID:TALK171818@talks.cam.ac.uk
DESCRIPTION:Many systems in plasma physics are most accurately described b
 y the Boltzmann equation\, which determines the time evolution of the phas
 e space distribution function of the system. Since this distribution funct
 ion is a function of six variables\, numerical schemes relying entirely on
  the discretization of the distribution function on a grid are often prohi
 bitively expensive in terms of memory and run-time complexity. Monte-Carlo
  methods have a computational cost that scales better with the number of d
 imensions of the system\, and are therefore favored. In particular\, one o
 f the most popular methods in plasma physics is the Particle-In-Cell metho
 d (PIC)\, which is a hybrid scheme combining a Monte-Carlo approach for ve
 locity space with a grid discretization for configuration space. A weaknes
 s of all PIC schemes is that they require a very large number of simulatio
 n particles in order to limit the error due to statistical noise inherentl
 y associated with any Monte-Carlo approach. Computer simulations of kineti
 c plasmas thus remain very expensive regardless of the chosen numerical sc
 heme\, often of the order of days and sometimes weeks on the largest super
 computers in the world.&nbsp\;In this talk\, I will discuss a rigorous met
 hod based on the sparse grids combination technique [1\,2] to reduce the n
 umerical noise in PIC simulations of plasmas\, and thus significantly redu
 ce the number of simulation particles without sacrificing the accuracy of 
 the solution.
LOCATION:Seminar Room 1\, Newton Institute
END:VEVENT
END:VCALENDAR