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Title: Learning constitutive relations from indirect observations using deep neural networks

Authors:
ORCiD logo; ORCiD logo; ; ORCiD logo
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1617427
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Journal of Computational Physics
Additional Journal Information:
Journal Name: Journal of Computational Physics Journal Volume: 416 Journal Issue: C; Journal ID: ISSN 0021-9991
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

Citation Formats

Huang, Daniel Z., Xu, Kailai, Farhat, Charbel, and Darve, Eric. Learning constitutive relations from indirect observations using deep neural networks. United States: N. p., 2020. Web. https://doi.org/10.1016/j.jcp.2020.109491.
Huang, Daniel Z., Xu, Kailai, Farhat, Charbel, & Darve, Eric. Learning constitutive relations from indirect observations using deep neural networks. United States. https://doi.org/10.1016/j.jcp.2020.109491
Huang, Daniel Z., Xu, Kailai, Farhat, Charbel, and Darve, Eric. Tue . "Learning constitutive relations from indirect observations using deep neural networks". United States. https://doi.org/10.1016/j.jcp.2020.109491.
@article{osti_1617427,
title = {Learning constitutive relations from indirect observations using deep neural networks},
author = {Huang, Daniel Z. and Xu, Kailai and Farhat, Charbel and Darve, Eric},
abstractNote = {},
doi = {10.1016/j.jcp.2020.109491},
journal = {Journal of Computational Physics},
number = C,
volume = 416,
place = {United States},
year = {2020},
month = {9}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.jcp.2020.109491

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