A multiscale, data-driven approach to identifying thermo-mechanically coupled laws—bottom-up with artificial neural networks
Document Type
Research Article
Publication Title
Computational Mechanics
Publication Date
4-7-2022
doi
10.1007/s00466-022-02161-2
First Page
163
Last Page
179
Recommended Citation
APA Citation
Xiang, Q.
Yang, H.
Elkhodary, K.
&
Qiu, H.
(2022). A multiscale, data-driven approach to identifying thermo-mechanically coupled laws—bottom-up with artificial neural networks. Computational Mechanics, 70, 163–179.
10.1007/s00466-022-02161-2
https://fount.aucegypt.edu/faculty_journal_articles/5738
MLA Citation
Xiang, Qian, et al.
"A multiscale, data-driven approach to identifying thermo-mechanically coupled laws—bottom-up with artificial neural networks." Computational Mechanics, vol. 70, 2022, pp. 163–179.
https://fount.aucegypt.edu/faculty_journal_articles/5738