Adaptive sampling accelerates the hybrid deviational particle simulations

TL;DR

This paper introduces an adaptive sampling strategy for hybrid deviational particle simulations that significantly accelerates computation by replacing rejection sampling with a more efficient adaptive approach, maintaining accuracy.

Contribution

The paper proposes the HDP-AS method, which uses adaptive sampling based on discrepancy estimation to improve the efficiency of deviational particle simulations.

Findings

HDP-AS is approximately ten times faster than the original HDP method.

HDP-AS maintains the same accuracy as the original method across various test problems.

Adaptive sampling effectively replaces rejection sampling in high-dimensional particle simulations.

Abstract

To avoid ineffective collisions between the equilibrium states, the hybrid method with deviational particles (HDP) has been proposed to integrate the Vlasov-Poisson-Landau system, while leaving a new issue in sampling deviational particles from the high-dimensional source term. In this paper, we present an adaptive sampling (AS) strategy that first adaptively reconstructs a piecewise constant approximation of the source term based on sequential clustering via discrepancy estimation, and then samples deviational particles directly from the resulting adaptive piecewise constant function without rejection. The mixture discrepancy, which can be easily calculated thanks to its explicit analytical expression, is employed as a measure of uniformity instead of the star discrepancy the calculation of which is NP-hard. The resulting method, dubbed the HDP-AS method, samples deviational particles through adaptive sampling instead of the acceptance-rejection method in the original HDP method. In the Landau damping, two stream instability, bump on tail and Rosenbluth's test problems, the HDP-AS method runs approximately ten times faster than the HDP method while keeping the same accuracy.