Sparse Grids based Adaptive Noise Reduction strategy for Particle-In-Cell schemes

TL;DR

This paper introduces a sparse grids based adaptive noise reduction method for particle-in-cell simulations that improves efficiency and accuracy by automatically adapting to simulation parameters and reducing computational costs.

Contribution

It presents a novel sparse grids filtering approach integrated into PIC codes, with an adaptive truncation technique to minimize errors and enhance performance.

Findings

Significant speedup over regular PIC methods.

Memory reduction while maintaining accuracy.

Effective in 2D diocotron instability and 3D electron dynamics.

Abstract

We propose a sparse grids based adaptive noise reduction strategy for electrostatic particle-in-cell (PIC) simulations. Our approach is based on the key idea of relying on sparse grids instead of a regular grid in order to increase the number of particles per cell for the same total number of particles, as first introduced in Ricketson and Cerfon (Plasma Phys. and Control. Fusion, 59(2), 024002). Adopting a new filtering perspective for this idea, we construct the algorithm so that it can be easily integrated into high performance large-scale PIC code bases. Unlike the physical and Fourier domain filters typically used in PIC codes, our approach automatically adapts to mesh size, number of particles per cell, smoothness of the density profile and the initial sampling technique. Thanks to the truncated combination technique, we can reduce the larger grid-based error of the standard sparse grids approach for non-aligned and non-smooth functions. We propose a heuristic based on formal error analysis for selecting the optimal truncation parameter at each time step, and develop a natural framework to minimize the total error in sparse PIC simulations. We demonstrate its efficiency and performance by means of two test cases: the diocotron instability in two dimensions, and the three-dimensional electron dynamics in a Penning trap. Our run time performance studies indicate that our new scheme can provide significant speedup and memory reduction as compared to regular PIC for achieving comparable accuracy in the charge density deposition.