Inverse design of topological metaplates for flexural waves with machine learning
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Otros documentos de la autoría: He, Liangshu; Wen, Zhihui; Jin, Yabin; Torrent, Daniel; Zhuang, Xiaoying; Rabczuk, Timon
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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Título
Inverse design of topological metaplates for flexural waves with machine learningFecha de publicación
2020-12-08Editor
Elsevier Ltd.ISSN
0264-1275Cita bibliográfica
He, L., Wen, Z., Jin, Y., Torrent, D., Zhuang, X., & Rabczuk, T. (2021). Inverse design of topological metaplates for flexural waves with machine learning. Materials & Design, 199, 109390.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
The mechanical analog to the topological insulators brings anomalous elastic wave properties which diversifies classic wave functions for potential broad applications. To obtain topological mechanical wave states with ... [+]
The mechanical analog to the topological insulators brings anomalous elastic wave properties which diversifies classic wave functions for potential broad applications. To obtain topological mechanical wave states with good quality at desired frequency ranges, it needs repetitive trials of different geometric parameters with traditional forward designs. In this work, we develop an inverse design of topological edge states for flexural wave using machine learning method which is promising for instantaneous design. Nonlinear mapping function from input targets to output desired parameters are adopted in artificial neural networks where the data sets for training are generated by the plane wave expansion method. Topological edge states are then realized and compared for different bandgap width conditions with such inverse designs, proving that wide bandgap can promote the confinement of the topological edge states. Finally, direction selective propagations with sharp turns are further demonstrated as anomalous wave behaviors. The machine learning inverse design of topological states for flexural wave provides an efficient way to design practical devices with targeted needs for potential applications such as signal processing, sensing and energy harvesting. [-]
Publicado en
Materials & Design, Vol. 199 (1 February 2021)Entidad financiadora
National Natural Science Foundation of China | Shanghai Institutions of Higher Learning
Código del proyecto o subvención
11902223 | 19PJ1410100 | 2019010106
Título del proyecto o subvención
Shanghai Pujiang Program | Fundamental Research Funds for the Central Universities and Shanghai municipal peak discipline program
Derechos de acceso
© 2020 The Author(s). Published by Elsevier Ltd
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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