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Gaussian process surrogates for failure detection: A Bayesian experimental design approach · Study

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ArticleGaussian process surrogates for failure detection: A Bayesian experimental design approach

Hongqiao Wang2016

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Articlebronze2016

Gaussian process surrogates for failure detection: A Bayesian experimental design approach

Hongqiao Wang · Guang Lin · Jinglai Li

32 citations·0/0 files stored·Journal of Computational Physics

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Probabilistic and Robust Engineering Design Optimal Experimental Design Methods Gaussian Processes and Bayesian Inference

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References

1989Design and Analysis of Computer Experiments 2001Estimation of small failure probabilities in high dimensions by subset simulation 1992Multivariate stochastic approximation using a simultaneous perturbation gradient approximation 1996Structural Reliability Methods 2011AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation 1990A fast and efficient response surface approach for structural reliability problems 2008Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions 2004Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach

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2024Bayesian reinforcement learning reliability analysis 2022Sequential Bayesian experimental design for estimation of extreme-event probability in stochastic input-to-response systems 2022CAMERA: A method for cost-aware, adaptive, multifidelity, efficient reliability analysis 2023Sampling-based adaptive design strategy for failure probability estimation

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Year2016

Typearticle

Published2016-02-27

VenueJournal of Computational Physics

ISSN-L0021-9991

Languageen

Citations32

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.jcp.2016.02.053

openalex_workW2270760299

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