Deep learning model to predict complex stress and strain fields in hierarchical composites-Reference-Cited by-同舟云学术

Deep learning model to predict complex stress and strain fields in hierarchical composites

Published:2021-04-09 Issue:15 Volume:7 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Yang Zhenze¹²^ORCID,Yu Chi-Hua¹³^ORCID,Buehler Markus J.¹⁴⁵^ORCID

Affiliation:

1. Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.

2. Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.

3. Department of Engineering Science, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan.

4. Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.

5. Center for Materials Science and Engineering, 77 Massachusetts Ave., Cambridge, MA 02139, USA.

Abstract

Deep learning predicts mechanical fields in hierarchical composites, as an alternative to conventional numerical methods.

Funder

Office of Naval Research

Army Research Office

AFOSR

MIT Quest

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference52 articles.

1. K. K. Chawla Composite Materials: Science and Engineering (Springer Science & Business Media 2012).

2. Biological and Bioinspired Composites with Spatially Tunable Heterogeneous Architectures

3. Nacre-inspired design of graphene oxide–polydopamine nanocomposites for enhanced mechanical properties and multi-functionalities

4. Dynamical fracture instabilities due to local hyperelasticity at crack tips

5. K. K. Chawla Ceramic Matrix Composites (Springer Science & Business Media 2013).

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