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Machine learning assisted design of shape-programmable 3D kirigami metamaterials

Engineering and Technology

Machine learning assisted design of shape-programmable 3D kirigami metamaterials

N. A. Alderete, N. Pathak, et al.

This innovative research by Nicolas A. Alderete, Nibir Pathak, and Horacio D. Espinosa presents a cutting-edge machine learning framework tailored for designing and controlling kirigami-based materials. By utilizing clustering, tandem neural networks, and symbolic regression, the framework predicts optimal kirigami cut layouts to meet specific design criteria, showcasing its effectiveness in developing shape-shifting metamaterials.

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