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    A Physics-Informed General Convolutional Network for the Computational Modeling of Materials With Damage 

    Source: Journal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 011:;page 111002-1
    Author(s): Janssen, Jake A.; Haikal, Ghadir; DeCarlo, Erin C.; Hartnett, Michael J.; Kirby, Matthew L.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Despite their effectiveness in modeling complex phenomena, the adoption of machine learning (ML) methods in computational mechanics has been hindered by the lack of availability of training datasets, limitations on the ...
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