Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks-Reference-Cited by-同舟云学术

Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks

Published:2019-05 Issue: Volume:162 Page:322-332
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Schwarzer Max,Rogan Bryce,Ruan Yadong,Song Zhengming,Lee Diana Y.,Percus Allon G.,Chau Viet T.,Moore Bryan A.,Rougier Esteban,Viswanathan Hari S.,Srinivasan Gowri

Funder

Los Alamos National Laboratory

Publisher

Elsevier BV

Subject

Computational Mathematics,General Physics and Astronomy,Mechanics of Materials,General Materials Science,General Chemistry,General Computer Science

Reference33 articles.

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5. Analysis of crack formation and crack growth in concrete by means of fracture mechanics and finite elements;Hillerborg;Cem. Concr. Res.,1976

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