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Data-driven physics-constrained recurrent neural networks for multiscale damage modeling of metallic alloys with process-induced porosity.

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Title: Data-driven physics-constrained recurrent neural networks for multiscale damage modeling of metallic alloys with process-induced porosity.
Authors: Deng, Shiguang1,2 (AUTHOR), Hosseinmardi, Shirin3 (AUTHOR), Wang, Libo1 (AUTHOR), Apelian, Diran1 (AUTHOR), Bostanabad, Ramin3 (AUTHOR) Raminb@uci.edu
Source: Computational Mechanics. Jul2024, Vol. 74 Issue 1, p191-221. 31p.
Database: Academic Search Ultimate
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  Data: Data-driven physics-constrained recurrent neural networks for multiscale damage modeling of metallic alloys with process-induced porosity.
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  Data: <searchLink fieldCode="AR" term="%22Deng%2C+Shiguang%22">Deng, Shiguang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hosseinmardi%2C+Shirin%22">Hosseinmardi, Shirin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Libo%22">Wang, Libo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Apelian%2C+Diran%22">Apelian, Diran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bostanabad%2C+Ramin%22">Bostanabad, Ramin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> Raminb@uci.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Computational+Mechanics%22">Computational Mechanics</searchLink>. Jul2024, Vol. 74 Issue 1, p191-221. 31p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=178130794
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        Value: 10.1007/s00466-023-02429-1
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        Text: English
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              Text: Jul2024
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              Y: 2024
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