Deep learning assisted inverse design of metamaterial microwave absorber
Xie Chen, Haonan Li, Chenyang Cui, Haodong Lei, Yingjie Sun, Chi Zhang, Yaqian Zhang, Hongxing Dong, Long Zhang
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.