Intelligent on-demand design of phononic metamaterials
نویسندگان
چکیده
Abstract With the growing interest in field of artificial materials, more advanced and sophisticated functionalities are required from phononic crystals acoustic metamaterials. This implies a high computational effort cost, still efficiency designs may be not sufficient. help third-wave intelligence technologies, design schemes these materials undergoing new revolution. As an important branch intelligence, machine learning paves way to technological innovations by stimulating exploration structural design. Machine provides powerful means achieving efficient accurate process exploring nonlinear physical patterns high-dimensional space, based on data sets candidate structures. Many algorithms, such as deep neural networks, unsupervised manifold clustering, reinforcement so forth, have been widely deeply investigated for In this review, we summarize recent works combination metamaterials learning. We provide overview Then discuss driven on-demand elastic waves functions, topological phases atomic-scale phonon properties. Finally, current state art prospective future development directions.
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ژورنال
عنوان ژورنال: Nanophotonics
سال: 2022
ISSN: ['2192-8606', '2192-8614']
DOI: https://doi.org/10.1515/nanoph-2021-0639