Accelerated Weight Histogram Method for Rare Event Simulations

TL;DR

The paper introduces the Accelerated Weight Histogram (AWH) method, an adaptive MCMC technique adapted from physics, for efficiently estimating rare failure probabilities in complex probabilistic models, demonstrating its effectiveness over existing methods.

Contribution

It presents the novel application of the AWH method to structural reliability and rare event simulation, expanding its use beyond molecular dynamics.

Findings

AWH effectively estimates rare failure probabilities.

AWH outperforms subset simulations in selected problems.

The method is adaptable to complex probabilistic models.

Abstract

We describe an adaptive Markov chain Monte Carlo method suitable for the estimation of rare failure probabilities in complex probabilistic models. This method, the Accelerated Weight Histogram (AWH) method, has its origin in statistical physics (Lidmar, 2012) and has successfully been applied to molecular dynamics simulations in biophysics. Here we introduce it in the context of structural reliability and demonstrate its usefulness for calculation of failure probabilities in some selected problems of varying degrees of complexity and compare with other established techniques, e.g., subset simulations.