Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions

We propose a method for the accurate estimation of rare event or failure probabilities for expensive to-evaluate numerical models in high dimensions. The proposed approach combines ideas from large deviation theory and adaptive importance sampling. The importance sampler uses a cross-entropy method to find an optimal Gaussian biasing distribution, and reuses all samples made throughout the process for both the target probability estimation and for updating the biasing distributions. Large deviation theory is used to find a good initial biasing distribution through the solution of an optimization problem. Additionally, it is used to identify a low-dimensional subspace that is most informative of the rare event probability. This subspace is used for the cross-entropy method, which is known to lose efficiency in higher dimensions. The proposed method does not require smoothing of indicator functions nor does it involve numerical tuning parameters. We compare the method with a state-of-the-art cross-entropy-based importance sampling scheme using three examples: a high-dimensional failure probability estimation benchmark, a problem governed by a diffusion equation, and a tsunami problem governed by the time-dependent shallow water system in one spatial dimension.

Automated summary

In this study, a method is proposed for accurate estimation of rare event or failure probabilities in high dimensions using expensive-to-evaluate numerical models. The proposed approach combines ideas from large deviation theory and adaptive importance sampling, incorporating a cross-entropy method to find an optimal Gaussian biasing distribution. The method does not require smoothing of indicator functions and achieves efficiency by identifying a low-dimensional subspace that is most informative of the rare event probability.