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KI-Net Conference Announcement

Seminar by Eric Sonnendrueker: Geometric electromagnetic PIC models

Nov 7, 2017

ICES, UT-Austin
University of Texas-Austin

ICES, UT Visitor Guide


ABSTRACT

A hamiltonian framework for the derivation of semi-discrete (continuous in time) Finite Element Particle In Cell approximations of the VlasovMaxwell equations was derived in [1]. It is based on a particle (Klimontovitch) discretization of the distribution function and a compatible Finite Element discretization of the grid quantities. The ideas introduced in [1] can be declined in different variants, choosing different discrete spaces for the fields or adding smoothing functions for the particles. Moreover, starting from such a semi-discretization, which yields a finite dimensional Hamiltonian structure dU dt = J (U)∇H(U), defined by a Poisson J (U) matrix and a hamiltonian H(U), several classes of different structure preserving time discretization can be derived: hamiltonian splitting methods as in [1], that preserve the Poisson structure, or discrete gradient methods that preserve exactly the hamiltonian. This procedure enables in particular to recover and generalize several wellknown explicit and implicit PIC algorithms. We are going in this talk to give an overview of the geometric ideas behind this structure and how they can be used to derive fully discrete particle in cell schemes with exact conservation of the Poisson structure, the energy and Gauss’ law.

REGISTRATION CLOSED

CONFIRMED PARTICIPANTS

NameAffiliation
Eric Sonnendr├╝ckerMax Planck Institute of Plasma Physics


FUNDING

A limited amount of travel and local lodging is available for researchers in the early stages of their career who want to attend the full program, especially for graduate students and post-doctoral fellows.

INFORMATION FOR PARTICIPANTS

ICES, UT Visitor Guide

ICES, UT-Austin (ICES, UT)
Peter O'Donnell Jr Bldg., 201 E 24th Street
Austin, TX

Email: gamba@math.utexas.edu

CONFERENCE POSTER

ACKNOWLEDGMENT

Funding provided by the NSF through the KI-net Grant.