A backward Monte-Carlo method for time-dependent runaway electron simulations

TL;DR

This paper introduces a backward Monte Carlo method based on stochastic differential equations to efficiently simulate time-dependent runaway electron dynamics, significantly reducing computational time while maintaining accuracy.

Contribution

A novel backward Monte Carlo algorithm utilizing backward stochastic differential equations for faster and more efficient runaway electron simulations.

Findings

Reduces the number of particles needed for accurate results

Unconditionally stable and easily parallelizable

Extensible to higher-dimensional problems

Abstract

Kinetic descriptions of runaway electrons (RE) are usually based on Fokker-Planck models that determine the probability distribution function (PDF) of RE in 2-dimensional momentum space. Despite of the simplification involved, the Fokker-Planck equation can rarely be solved analytically and direct numerical approaches (e.g., continuum and particle-based Monte Carlo (MC)) can be time consuming, especially in the computation of asymptotic-type observables including the runaway probability, the slowing-down and runaway mean times, and the energy limit probability. Here we present a novel backward MC approach to these problems based on backward stochastic differential equations (BSDEs) that describe the dynamics of the runaway probability by means of the Feynman-Kac theory. The key ingredient of the backward MC algorithm is to place all the particles in a runaway state and simulate them backward from the terminal time to the initial time. As such, our approach can provide much faster convergence than direct MC methods (by significantly reducing the number of particles required to achieve a prescribed accuracy) while at the same time maintaining the advantages of particle-based methods (compared to continuum approaches). The proposed algorithm is unconditionally stable, can be parallelized as easy as the direct MC method, and its extension to dimensions higher than two is straightforward, thus paving the way for conducting large-scale RE simulation.