1. Deep learning assisted inverse design of metamaterial microwave absorber.
- Author
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Xie, Chen, Li, Haonan, Cui, Chenyang, Lei, Haodong, Sun, Yingjie, Zhang, Chi, Zhang, Yaqiang, Dong, Hongxing, and Zhang, Long
- Subjects
DEEP learning ,METAMATERIALS ,MICROWAVES - Abstract
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. [ABSTRACT FROM AUTHOR]- Published
- 2023
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