Adaptive Design of Experiments for Conservative Estimation of Excursion Sets

Abstract : We consider a Gaussian process model trained on few evaluations of an expensive-to-evaluate deterministic function and we study the problem of estimating a fixed excursion set of this function. We review the concept of conservative estimates, recently introduced in this framework, and, in particular, we focus on estimates based on Vorob'ev quantiles. We present a method that sequentially selects new evaluations of the function in order to reduce the uncertainty on such estimates. The sequential strategies are first benchmarked on artificial test cases generated from Gaussian process realizations in two and five dimensions, and then applied to two reliability engineering test cases.
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Contributor : Azzimonti Dario <>
Submitted on : Tuesday, January 10, 2017 - 11:54:54 AM
Last modification on : Tuesday, February 18, 2020 - 10:54:01 AM
Long-term archiving on: Tuesday, April 11, 2017 - 2:21:20 PM


Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License


  • HAL Id : hal-01379642, version 2
  • ARXIV : 1611.07256


Dario Azzimonti, David Ginsbourger, Clément Chevalier, Julien Bect, Yann Richet. Adaptive Design of Experiments for Conservative Estimation of Excursion Sets. 2017. ⟨hal-01379642v2⟩



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