Deep learning model to predict complex stress and strain fields in hierarchical composites
Name
sciadv.abd7416.pdf
Description
Published version
Size
4.29 MB
Format
Adobe PDF
Checksum (MD5)
d134d8a9f1c9e3b2868f61507ee2dc3b
Author(s)
Yang, Zhenze
Yu, Chi-Hua
Buehler, Markus J.
Date Issued
April 2021
Journal
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Citation
Yang, Zhenze, Yu, Chi-Hua and Buehler, Markus J. 2021. "Deep learning model to predict complex stress and strain fields in hierarchical composites." Science Advances, 7 (15).
Version
Final published version
Abstract
Materials-by-design is a paradigm to develop previously unknown high-performance materials. However, finding materials with superior properties is often computationally or experimentally intractable because of the astronomical number of combinations in design space. Here we report an AI-based approach, implemented in a game theory–based conditional generative adversarial neural network (cGAN), to bridge the gap between a material’s microstructure—the design space—and physical performance. Our end-to-end deep learning model predicts physical fields like stress or strain directly from the material microstructure geometry, and reaches an astonishing accuracy not only for predicted field data but also for derivative material property predictions. Furthermore, the proposed approach offers extensibility by predicting complex materials behavior regardless of component shapes, boundary conditions, and geometrical hierarchy, providing perspectives of performing physical modeling and simulations. The method vastly improves the efficiency of evaluating physical properties of hierarchical materials directly from the geometry of its structural makeup.
MIT Department
Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Center for Computational Science and Engineering
Massachusetts Institute of Technology. Center for Materials Science and Engineering
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
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DOI of Published Version
https://doi.org/10.1126/sciadv.abd7416
