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Abstract
This paper introduces a machine learning framework for the design and control of kirigami-based materials. The framework combines clustering, tandem neural networks, and symbolic regression to predict kirigami cut layouts that achieve specific design constraints. The approach is experimentally validated through various applications, demonstrating its utility in creating shape-shifting kirigami metamaterials.
Publisher
npj Computational Materials
Published On
Authors
Nicolas A. Alderete, Nibir Pathak, Horacio D. Espinosa
Tags
machine learning
kirigami
metamaterials
neural networks
symbolic regression
design constraints

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