Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures

查看全文 作　　者：Yashar [1]Kiarashinejad;Sajjad [1]Abdollahramezani;Ali [1]Adibi

机构地区：[1]School of Electrical and Computer Engineering,Georgia Institute of Technology,778 Atlantic Drive NW,Atlanta,GA 30332,USA

出　　处：《npj Computational Materials》索引2020年第1期,共12页

基　　金：This work was funded by Defense Advanced Research Projects Agency(DARPA)(D19AC00001,Dr.M.Fiddy);in part by the Office of Naval Research(ONR)(N00014-18-1-2055,Dr.B.Bennett). 摘　　要：In this paper,we demonstrate a computationally efficient new approach based on deep learning(DL)techniques for analysis,design and optimization of electromagnetic(EM)nanostructures.We use the strong correlation among features of a generic EM problem to considerably reduce the dimensionality of the problem and thus,the computational complexity,without imposing considerable errors.By employing the dimensionality reduction concept using the more recently demonstrated autoencoder technique,we redefine the conventional many-to-one design problem in EM nanostructures into a one-to-one problem plus a much simpler many-to-one problem,which can be simply solved using an analytic formulation. 关键词：DEEP LEARNING dimensionality

相关文献

参考文献（60）
引证文献（9）
耦合文献（0）

参考文献(60)

引证文献(9)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures

参考文献(60)

引证文献(9)

用户提示