On Geometric Fourier Particle In Cell Methods

TL;DR

This paper introduces a unifying variational framework for spectral electromagnetic particle schemes, proposing a novel Hamiltonian-preserving spectral Particle-In-Cell method called Fourier-GEMPIC, which extends existing geometric PIC approaches.

Contribution

It develops a new variational spectral PIC method with discrete Hamiltonian structure, extending GEMPIC to spectral solvers and introducing Fourier-GEMPIC with explicit, structure-preserving time discretization.

Findings

The Fourier-GEMPIC method preserves Hamiltonian structure and Gauss laws.

Explicit time-stepping schemes are derived that maintain key physical invariants.

The framework allows for filtering to reduce aliasing errors in spectral PIC methods.

Abstract

In this article we describe a unifying framework for variational electromagnetic particle schemes of spectral type, and we propose a novel spectral Particle-In-Cell (PIC) scheme that preserves a discrete Hamiltonian structure. Our work is based on a new abstract variational derivation of particle schemes which builds on a de Rham complex where Low's Lagrangian is discretized using a particle approximation of the distribution function. In this framework, which extends the recent Finite Element based Geometric Electromagnetic PIC (GEMPIC) method to a variety of field solvers, the discretization of the electromagnetic potentials and fields is represented by a de Rham sequence of compatible spaces, and the particle-field coupling procedure is described by approximation operators that commute with the differential operators in the sequence. In particular, for spectral Maxwell solvers the choice of truncated $L^2$ projections using continuous Fourier transform coefficients for the commuting approximation operators yields the gridless Particle-in-Fourier method, whereas spectral Particle-in-Cell methods are obtained by using discrete Fourier transform coefficients computed from a grid. By introducing a new sequence of spectral pseudo-differential approximation operators, we then obtain a novel variational spectral PIC method with discrete Hamiltonian structure that we call Fourier-GEMPIC. Fully discrete schemes are then derived using a Hamiltonian splitting procedure, leading to explicit time steps that preserve the Gauss laws and the discrete Poisson bracket associated with the Hamiltonian structure. These explicit steps share many similarities with standard spectral PIC methods. As arbitrary filters are allowed in our framework, we also discuss aliasing errors and study a natural back-filtering procedure to mitigate the damping caused by anti-aliasing smoothing particle shapes.