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Title: Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice

Authors:
; ORCiD logo;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1329337
Grant/Contract Number:
AC05-76RL01830
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Journal of Computational Physics
Additional Journal Information:
Journal Volume: 321; Journal Issue: C; Related Information: CHORUS Timestamp: 2017-10-06 09:02:51; Journal ID: ISSN 0021-9991
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

Citation Formats

Li, Weixuan, Lin, Guang, and Li, Bing. Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice. United States: N. p., 2016. Web. doi:10.1016/j.jcp.2016.05.040.
Li, Weixuan, Lin, Guang, & Li, Bing. Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice. United States. doi:10.1016/j.jcp.2016.05.040.
Li, Weixuan, Lin, Guang, and Li, Bing. Thu . "Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice". United States. doi:10.1016/j.jcp.2016.05.040.
@article{osti_1329337,
title = {Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice},
author = {Li, Weixuan and Lin, Guang and Li, Bing},
abstractNote = {},
doi = {10.1016/j.jcp.2016.05.040},
journal = {Journal of Computational Physics},
number = C,
volume = 321,
place = {United States},
year = {Thu Sep 01 00:00:00 EDT 2016},
month = {Thu Sep 01 00:00:00 EDT 2016}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.jcp.2016.05.040

Citation Metrics:
Cited by: 5works
Citation information provided by
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