Intelligent on-demand design of phononic metamaterials
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Otros documentos de la autoría: Jin, Yabin; He, Liangshu; Wen, Zhihui; Mortazavi, Bohayra; Guo, Hongwei; Torrent, Daniel; Djafari Rouhani, Bahram; Rabczuk, Timon; Zhuang, Xiaoying; Li, Yan
Metadatos
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INVESTIGACIONMetadatos
Título
Intelligent on-demand design of phononic metamaterialsAutoría
Fecha de publicación
2022-01-04Editor
De GruyterISSN
2192-8606; 2192-8614Cita bibliográfica
Jin, Y., He, L., Wen, Z., Mortazavi, B., Guo, H., Torrent, D., Djafari-Rouhani, B., Rabczuk, T., Zhuang, X. & Li, Y. (2022). Intelligent on-demand design of phononic metamaterials. Nanophotonics, 11(3), 439-460Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
With the growing interest in the field of artificial materials, more advanced and sophisticated functionalities are required from phononic crystals and acoustic metamaterials. This implies a high computational effort ... [+]
With the growing interest in the field of artificial materials, more advanced and sophisticated functionalities are required from phononic crystals and acoustic metamaterials. This implies a high computational effort and cost, and still the efficiency of the designs may be not sufficient. With the help of third-wave artificial intelligence technologies, the design schemes of these materials are undergoing a new revolution. As an important branch of artificial intelligence, machine learning paves the way to new technological innovations by stimulating the exploration of structural design. Machine learning provides a powerful means of achieving an efficient and accurate design process by exploring nonlinear physical patterns in high-dimensional space, based on data sets of candidate structures. Many advanced machine learning algorithms, such as deep neural networks, unsupervised manifold clustering, reinforcement learning and so forth, have been widely and deeply investigated for structural design. In this review, we summarize the recent works on the combination of phononic metamaterials and machine learning. We provide an overview of machine learning on structural design. Then discuss machine learning driven on-demand design of phononic metamaterials for acoustic and elastic waves functions, topological phases and atomic-scale phonon properties. Finally, we summarize the current state of the art and provide a prospective of the future development directions. [-]
Publicado en
Nanophotonics, Volume 11 Issue 3 (2022)Entidad financiadora
National Key R&D Program of China | National Natural Science Foundation of China | Ramon y Cajal | Ministerio de Ciencia, Innovación y Universidades | Universitat Jaume I
Código del proyecto o subvención
2020YFA0211402 | 11902223 | RYC-2016-21188 | RTI2018-093921-AC42 | UJI-A2018-08
Derechos de acceso
info:eu-repo/semantics/openAccess
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