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

Authors:
ORCiD logo; ; ORCiD logo
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1348258
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Journal of Computational Physics
Additional Journal Information:
Journal Name: Journal of Computational Physics Journal Volume: 313 Journal Issue: C; Journal ID: ISSN 0021-9991
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

Citation Formats

Wang, Hongqiao, Lin, Guang, and Li, Jinglai. Gaussian process surrogates for failure detection: A Bayesian experimental design approach. United States: N. p., 2016. Web. doi:10.1016/j.jcp.2016.02.053.
Wang, Hongqiao, Lin, Guang, & Li, Jinglai. Gaussian process surrogates for failure detection: A Bayesian experimental design approach. United States. doi:10.1016/j.jcp.2016.02.053.
Wang, Hongqiao, Lin, Guang, and Li, Jinglai. Sun . "Gaussian process surrogates for failure detection: A Bayesian experimental design approach". United States. doi:10.1016/j.jcp.2016.02.053.
@article{osti_1348258,
title = {Gaussian process surrogates for failure detection: A Bayesian experimental design approach},
author = {Wang, Hongqiao and Lin, Guang and Li, Jinglai},
abstractNote = {},
doi = {10.1016/j.jcp.2016.02.053},
journal = {Journal of Computational Physics},
number = C,
volume = 313,
place = {United States},
year = {2016},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1016/j.jcp.2016.02.053

Citation Metrics:
Cited by: 2 works
Citation information provided by
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