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FE-PINNs: Finite-element-based physics-informed neural networks for surrogate modeling.

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Title: FE-PINNs: Finite-element-based physics-informed neural networks for surrogate modeling.
Authors: Sunil, Pranav1 (AUTHOR), Sills, Ryan B.1 (AUTHOR) ryan.sills@rutgers.edu
Source: APL Machine Learning. Mar2026, Vol. 4 Issue 1, p1-15. 15p.
Database: Academic Search Ultimate
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  Data: FE-PINNs: Finite-element-based physics-informed neural networks for surrogate modeling.
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  Data: <searchLink fieldCode="AR" term="%22Sunil%2C+Pranav%22">Sunil, Pranav</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sills%2C+Ryan+B%2E%22">Sills, Ryan B.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ryan.sills@rutgers.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22APL+Machine+Learning%22">APL Machine Learning</searchLink>. Mar2026, Vol. 4 Issue 1, p1-15. 15p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=192684611
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        Value: 10.1063/5.0299671
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      – Code: eng
        Text: English
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        PageCount: 15
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      – TitleFull: FE-PINNs: Finite-element-based physics-informed neural networks for surrogate modeling.
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              Text: Mar2026
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              Y: 2026
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