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Coupled Bayesian Identification of Residual Stress and Fracture Strength in Thin-Film Fragmentation: A Physics-Informed Neural Network Framework with Synthetic Validation of Interface Adhesion Energy.

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Title: Coupled Bayesian Identification of Residual Stress and Fracture Strength in Thin-Film Fragmentation: A Physics-Informed Neural Network Framework with Synthetic Validation of Interface Adhesion Energy.
Authors: Li, Jun1 (AUTHOR), Li, Linan2 (AUTHOR), Wang, Zhiyong2,3 (AUTHOR), Li, Chuanwei1,2 (AUTHOR), Wang, Shibin2 (AUTHOR), Kang, Kai3 (AUTHOR)
Source: Materials (1996-1944). Jul2026, Vol. 19 Issue 13, p2824. 17p.
Database: Academic Search Ultimate
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An: 195440749
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PubType: Academic Journal
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  Data: Coupled Bayesian Identification of Residual Stress and Fracture Strength in Thin-Film Fragmentation: A Physics-Informed Neural Network Framework with Synthetic Validation of Interface Adhesion Energy.
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  Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. Jul2026, Vol. 19 Issue 13, p2824. 17p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=195440749
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        Value: 10.3390/ma19132824
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 2824
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      – TitleFull: Coupled Bayesian Identification of Residual Stress and Fracture Strength in Thin-Film Fragmentation: A Physics-Informed Neural Network Framework with Synthetic Validation of Interface Adhesion Energy.
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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              Value: 19
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