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) |
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Format: | Artikel |
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. |
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ISSN: | 0003-6951 1077-3118 |
DOI: | 10.1063/5.0171437 |