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

Authors:
; ORCiD logo ;
Publication Date:
Grant/Contract Number:
AC05-76RL01830
Type:
Publisher's Accepted Manuscript
Journal Name:
Journal of Computational Physics
Additional Journal Information:
Journal Volume: 321; Journal Issue: C; Related Information: CHORUS Timestamp: 2018-09-11 12:52:34; Journal ID: ISSN 0021-9991
Publisher:
Elsevier
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
OSTI Identifier:
1329337

Li, Weixuan, Lin, Guang, and Li, Bing. Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice. United States: N. p., 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. 2016. "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 = {2016},
month = {9}
}