Forward and inverse design of kirigami via supervised autoencoder

Machine learning (ML) methods have recently been used as forward solvers to predict the mechanical properties of composite materials. Here, we use a supervised autoencoder (SAE) to perform the inverse design of graphene kirigami, where predicting the ultimate stress or strain under tensile loading i...

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Bibliographic Details

Published in:	Physical review research Vol. 2; no. 4; p. 042006
Main Authors:	Hanakata, Paul Z., Cubuk, Ekin D., Campbell, David K., Park, Harold S.
Format:	Journal Article
Language:	English
Published:	American Physical Society 12.10.2020
ISSN:	2643-1564, 2643-1564
Online Access:	Get full text
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