Metamodel-based importance sampling for the simulation of rare events
Abstract
In the field of structural reliability, the Monte-Carlo estimator is considered as the reference probability estimator. However, it is still untractable for real engineering cases since it requires a high number of runs of the model. In order to reduce the number of computer experiments, many other approaches known as reliability methods have been proposed. A certain approach consists in replacing the original experiment by a surrogate which is much faster to evaluate. Nevertheless, it is often difficult (or even impossible) to quantify the error made by this substitution. In this paper an alternative approach is developed. It takes advantage of the kriging meta-modeling and importance sampling techniques. The proposed alternative estimator is finally applied to a finite element based structural reliability analysis.
Cite
@article{arxiv.1104.3476,
title = {Metamodel-based importance sampling for the simulation of rare events},
author = {V. Dubourg and F. Deheeger and B. Sudret},
journal= {arXiv preprint arXiv:1104.3476},
year = {2015}
}
Comments
8 pages, 3 figures, 1 table. Preprint submitted to ICASP11 Mini-symposia entitled "Meta-models/surrogate models for uncertainty propagation, sensitivity and reliability analysis"