A subset multicanonical Monte Carlo method for simulating rare failure events

TL;DR

This paper introduces a novel subset multicanonical Monte Carlo method that efficiently estimates extremely small failure probabilities in engineering systems, outperforming existing methods and providing full distribution insights.

Contribution

The paper presents a new subset MMC algorithm that adaptively combines subset simulation and multicanonical Monte Carlo to improve sampling efficiency for rare failure events.

Findings

Significantly more efficient than SS and MMC methods.

Able to reconstruct the full distribution function.

Effective for failure probabilities as low as 10^{-10}.

Abstract

Estimating failure probabilities of engineering systems is an important problem in many engineering fields. In this work we consider such problems where the failure probability is extremely small (e.g $\leq10^{-10}$). In this case, standard Monte Carlo methods are not feasible due to the extraordinarily large number of samples required. To address these problems, we propose an algorithm that combines the main ideas of two very powerful failure probability estimation approaches: the subset simulation (SS) and the multicanonical Monte Carlo (MMC) methods. Unlike the standard MMC which samples in the entire domain of the input parameter in each iteration, the proposed subset MMC algorithm adaptively performs MMC simulations in a subset of the state space and thus improves the sampling efficiency. With numerical examples we demonstrate that the proposed method is significantly more efficient than both of the SS and the MMC methods. Moreover, the proposed algorithm can reconstruct the complete distribution function of the parameter of interest and thus can provide more information than just the failure probabilities of the systems.