典型文献
Machine-learning-empowered multispectral metafilm with reduced radar cross section,low infrared emissivity,and visible transparency
文献摘要：
For camouflage applications,the performance requirements for metamaterials in different electromagnetic spectra are usually contradictory,which makes it difficult to develop satisfactory design schemes with multispectral com-patibility.Fortunately,empowered by machine learning,metamaterial design is no longer limited to directly solving Maxwell's equations.The design schemes and experiences of metamaterials can be analyzed,summarized,and learned by computers,which will significantly improve the design efficiency for the sake of practical engineer-ing applications.Here,we resort to the machine learning to solve the multispectral compatibility problem of metamaterials and demonstrate the design of a new metafilm with multiple mechanisms that can realize small microwave scattering,low infrared emissivity,and visible transparency simultaneously using a multilayer back-propagation neural network.The rapid evolution of structural design is realized by establishing a mapping be-tween spectral curves and structural parameters.By training the network with different materials,the designed network is more adaptable.Through simulations and experimental verifications,the designed architecture has good accuracy and robustness.This paper provides a facile method for fast designs of multispectral metafilms that can find wide applications in satellite solar panels,aircraft windows,and others.
文献关键词：
中图分类号：
作者姓名：
RUICHAO ZHU;JIAFU WANG;JINMING JIANG;CUILIAN XU;CHE LIU;YUXIANG JIA;SAI SUI;ZHONGTAO ZHANG;TONGHAO LIU;ZUNTIAN CHU;JUN WANG;TIE JUN CUI;SHAOBO QU
作者机构：
Shaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices,Air Force Engineering University,Xi'an 710051,China;Institute of Electromagnetic Space,Southeast University,Nanjing 210096,China;State Key Laboratory of Millimeter Wave,Southeast University,Nanjing 210096,China
文献出处：
引用格式：
[1]RUICHAO ZHU;JIAFU WANG;JINMING JIANG;CUILIAN XU;CHE LIU;YUXIANG JIA;SAI SUI;ZHONGTAO ZHANG;TONGHAO LIU;ZUNTIAN CHU;JUN WANG;TIE JUN CUI;SHAOBO QU-.Machine-learning-empowered multispectral metafilm with reduced radar cross section,low infrared emissivity,and visible transparency)[J].光子学研究（英文）,2022(05):1146-1156
A类：
metafilm,metafilms
B类：
Machine,learning,empowered,multispectral,reduced,radar,cross,section,low,infrared,emissivity,visible,transparency,camouflage,applications,performance,requirements,metamaterials,different,electromagnetic,usually,contradictory,which,makes,difficult,develop,satisfactory,schemes,Fortunately,by,machine,no,longer,limited,directly,solving,Maxwell,equations,experiences,be,analyzed,summarized,learned,computers,will,significantly,improve,efficiency,sake,practical,engineer,Here,resort,solve,compatibility,problem,demonstrate,new,multiple,mechanisms,that,small,microwave,scattering,simultaneously,using,multilayer,back,propagation,neural,network,rapid,evolution,structural,realized,establishing,mapping,tween,curves,parameters,By,training,designed,more,adaptable,Through,simulations,experimental,verifications,architecture,has,good,accuracy,robustness,This,paper,provides,facile,method,fast,designs,find,wide,satellite,solar,panels,aircraft,windows,others
AB值：
0.602883
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