Generative machine learning models face challenges in creating kirigami metamaterials due to complex design restrictions. The study evaluates popular generative models and their performance in generating kirigami structures. It highlights the limitations of relying on Euclidean distance as a metric for assessing similarity in intricate geometries like kirigami metamaterials.
The research explores the impact of different maximum rotation values on the ability of generative algorithms to avoid intersections and generate admissible designs. Results show varying degrees of success among different generative models, with some struggling to capture complex design space constraints effectively.
The study emphasizes the need for further investigation into developing new generative models tailored for mechanical metamaterials with intricate design spaces like kirigami structures.
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by Gerrit Felsc... at arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.19196.pdfDeeper Inquiries