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Deep learning assisted inverse design of metamaterial microwave absorber.
- Source :
-
Applied Physics Letters . 10/30/2023, Vol. 123 Issue 18, p1-7. 7p. - Publication Year :
- 2023
-
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]
- Subjects :
- *DEEP learning
*METAMATERIALS
*MICROWAVES
Subjects
Details
- Language :
- English
- ISSN :
- 00036951
- Volume :
- 123
- Issue :
- 18
- Database :
- Academic Search Index
- Journal :
- Applied Physics Letters
- Publication Type :
- Academic Journal
- Accession number :
- 173433788
- Full Text :
- https://doi.org/10.1063/5.0171437