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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Adaptive sparse grids to reduce noise in numerical
simulations of kinetic plasmas - Antoine Cerfon (
Courant Institute of Mathematical Sciences\, New Y
ork University)
DTSTART;TZID=Europe/London:20220425T100000
DTEND;TZID=Europe/London:20220425T110000
UID:TALK171818AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/171818
DESCRIPTION:Many systems in plasma physics are most accurately
described by the Boltzmann equation\, which deter
mines the time evolution of the phase space distri
bution function of the system. Since this distribu
tion function is a function of six variables\, num
erical schemes relying entirely on the discretizat
ion of the distribution function on a grid are oft
en prohibitively expensive in terms of memory and
run-time complexity. Monte-Carlo methods have a co
mputational cost that scales better with the numbe
r of dimensions of the system\, and are therefore
favored. In particular\, one of the most popular m
ethods in plasma physics is the Particle-In-Cell m
ethod (PIC)\, which is a hybrid scheme combining a
Monte-Carlo approach for velocity space with a gr
id discretization for configuration space. A weakn
ess of all PIC schemes is that they require a very
large number of simulation particles in order to
limit the error due to statistical noise inherentl
y associated with any Monte-Carlo approach. Comput
er simulations of kinetic plasmas thus remain very
expensive regardless of the chosen numerical sche
me\, often of the order of days and sometimes week
s on the largest supercomputers in the world.
\;In this talk\, I will discuss a rigorous method
based on the sparse grids combination technique [1
\,2] to reduce the numerical noise in PIC simulati
ons of plasmas\, and thus significantly reduce the
number of simulation particles without sacrificin
g the accuracy of the solution.
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:
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