Journal ArticleDOI
Ultrathin acoustic absorbing metasurface based on deep learning approach
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This article is published in Smart Materials and Structures.The article was published on 2021-06-18. It has received 33 citations till now. The article focuses on the topics: Deep learning.read more
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Intelligent on-demand design of phononic metamaterials
Ya-Qiu Jin,Liangshu He,Zhihui Wen,Bohayra Mortazavi,Hongwei Guo,Daniel Torrent,Bahram Djafari-Rouhani,Timon Rabczuk,Xiaoying Zhuang,Yan Li +9 more
TL;DR: This review of the recent works on the combination of phononic metamaterials and machine learning provides an overview of machine learning on structural design, and discusses machine learning driven on-demand design of phononymaterials for acoustic and elastic waves functions, topological phases and atomic-scale phonon properties.
Journal ArticleDOI
Design of Acoustic/Elastic Phase Gradient Metasurfaces: Principles, Functional Elements, Tunability and Coding
TL;DR: In this article , the authors summarize recent developments in acoustic/elastic phase gradient metamaterials, including design principles, design of functional elements, wave field manipulation with applications, and design of tunable metasurfaces.
Journal ArticleDOI
Broadband Coding Metasurfaces with 2-bit Manipulations
TL;DR: In this paper , a broadband acoustic coding metasurfaces (BACMs) whose units are designed by the bottom-up topology optimization method are presented, and the 1-bit and 2-bit coding units with out-of-phase responses are designed.
Journal ArticleDOI
Machine Learning and Deep Learning in Phononic Crystals and Metamaterials A Review
Muhammad,John Kennedy,C.-W. Lim +2 more
TL;DR: In this article , the authors present a state-of-the-art literature survey in machine learning and deep learning based phononic crystals and metamaterial designs by giving historical context, discussing network architectures and working principles.
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SAP-Net: Deep learning to predict sound absorption performance of metaporous materials
TL;DR: In this article, the authors proposed a deep convolutional neural network (SAP-net) to predict the sound absorption coefficient at a specific frequency of an input image representing the topological structure of metaporous materials.
References
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Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Journal ArticleDOI
Approximation by superpositions of a sigmoidal function
TL;DR: It is demonstrated that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more
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Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
TL;DR: A shoulder strap retainer having a base to be positioned on the exterior shoulder portion of a garment with securing means attached to the undersurface of the base for removably securing the base to the exterior shoulders portion of the garment.
Journal ArticleDOI
Ultrasonic metamaterials with negative modulus
Nicholas X. Fang,Dongjuan Xi,Jianyi Xu,Muralidhar Ambati,Werayut Srituravanich,Cheng Sun,Xiang Zhang +6 more
TL;DR: A new class of ultrasonic metamaterials consisting of an array of subwavelength Helmholtz resonators with designed acoustic inductance and capacitance with an effective dynamic modulus with negative values near the resonance frequency is reported.
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