Consensus-Based Rare Event Estimation

Abstract

SIAM Journal on Scientific Computing, Volume 46, Issue 3, Page A1487-A1513, June 2024.
Abstract. In this paper, we introduce a new algorithm for rare event estimation based on adaptive importance sampling. We consider a smoothed version of the optimal importance sampling density, which is approximated by an ensemble of interacting particles. The particle dynamics is governed by a McKean–Vlasov stochastic differential equation, which was introduced and analyzed in [Carrillo et al., Stud. Appl. Math., 148 (2022), pp. 1069–1140] for consensus-based sampling and optimization of posterior distributions arising in the context of Bayesian inverse problems. We develop automatic updates for the internal parameters of our algorithm. This includes a novel time step size controller for the exponential Euler method, which discretizes the particle dynamics. The behavior of all parameter updates depends on easy to interpret accuracy criteria specified by the user. We show in numerical experiments that our method is competitive to state-of-the-art adaptive importance sampling algorithms for rare event estimation, namely a sequential importance sampling method and the ensemble Kalman filter for rare event estimation. Reproducibility of computational results. This paper has been awarded the “SIAM Reproducibility Badge: Code and data available” as a recognition that the authors have followed reproducibility principles valued by SISC and the scientific computing community. Code and data that allow readers to reproduce the results in this paper are available at https://github.com/AlthausKonstantin/rareeventestimation/tree/master/docs/figures_paper and in the supplementary materials (rareeventestimation-0.3.0.zip [9.66MB]).