Deep learning assisted inverse design of metamaterial microwave absorber

To accelerate the design of metamaterial microwave absorbers (MMAs), in this work, we developed a deep neural network model to predict the spectrum based on the known structural parameters at the beginning. Then, a tandem network was constructed, which can predict the geometries of an unknown MMA ba...

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Veröffentlicht in:Applied physics letters 2023-10, Vol.123 (18)
Hauptverfasser: Xie, Chen, Li, Haonan, Cui, Chenyang, Lei, Haodong, Sun, Yingjie, Zhang, Chi, Zhang, Yaqiang, Dong, Hongxing, Zhang, Long
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Sprache:eng
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Zusammenfassung:To accelerate the design of metamaterial microwave absorbers (MMAs), in this work, we developed a deep neural network model to predict the spectrum based on the known structural parameters at the beginning. Then, a tandem network was constructed, which can predict the geometries of an unknown MMA based on a desired absorption characteristics with a small mean square errors of validation set (8.3 × 10−4). With the help of the tandem network, a dual band absorber that achieves an absorption rate greater than 85% in the range of 5.1–14 GHz was obtained. By comparing with traditional methods, the demonstrated methodology can greatly accelerate the whole process and realize an inverse design.
ISSN:0003-6951
1077-3118
DOI:10.1063/5.0171437