Sequential design of computer experiments for the estimation of a probability of failure - CentraleSupélec Access content directly
Journal Articles Statistics and Computing Year : 2012

Sequential design of computer experiments for the estimation of a probability of failure

Abstract

This paper deals with the problem of estimating the volume of the excursion set of a function $f:\mathbb{R}^d \to \mathbb{R}$ above a given threshold, under a probability measure on $\RR^d$ that is assumed to be known. In the industrial world, this corresponds to the problem of estimating a probability of failure of a system. When only an expensive-to-simulate model of the system is available, the budget for simulations is usually severely limited and therefore classical Monte Carlo methods ought to be avoided. One of the main contributions of this article is to derive SUR (stepwise uncertainty reduction) strategies from a Bayesian-theoretic formulation of the problem of estimating a probability of failure. These sequential strategies use a Gaussian process model of $f$ and aim at performing evaluations of $f$ as efficiently as possible to infer the value of the probability of failure. We compare these strategies to other strategies also based on a Gaussian process model for estimating a probability of failure.
Fichier principal
Vignette du fichier
StatAndComp.pdf (1.09 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00689580 , version 1 (19-04-2012)

Identifiers

Cite

Julien Bect, David Ginsbourger, Ling Li, Victor Picheny, Emmanuel Vazquez. Sequential design of computer experiments for the estimation of a probability of failure. Statistics and Computing, 2012, 22 (3), pp.773-793. ⟨10.1007/s11222-011-9241-4⟩. ⟨hal-00689580⟩
266 View
326 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More