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Deep Learning-Based Design Method for Acoustic Metasurface Dual-Feature Fusion.

Authors :
Lv, Qiang
Zhao, Huanlong
Huang, Zhen
Hao, Guoqiang
Chen, Wei
Source :
Materials (1996-1944); May2024, Vol. 17 Issue 9, p2166, 15p
Publication Year :
2024

Abstract

Existing research in metasurface design was based on trial-and-error high-intensity iterations and requires deep acoustic expertise from the researcher, which severely hampered the development of the metasurface field. Using deep learning enabled the fast and accurate design of hypersurfaces. Based on this, in this paper, an integrated learning approach was first utilized to construct a model of the forward mapping relationship between the hypersurface physical structure parameters and the acoustic field, which was intended to be used for data enhancement. Then a dual-feature fusion model (DFCNN) based on a convolutional neural network was proposed, in which the first feature was the high-dimensional nonlinear features extracted using a data-driven approach, and the second feature was the physical feature information of the acoustic field mined using the model. A convolutional neural network was used for feature fusion. A genetic algorithm was used for network parameter optimization. Finally, generalization ability verification was performed to prove the validity of the network model. The results showed that 90% of the integrated learning models had an error of less than 3 dB between the real and predicted sound field data, and 93% of the DFCNN models could achieve an error of less than 5 dB in the local sound field intensity. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19961944
Volume :
17
Issue :
9
Database :
Complementary Index
Journal :
Materials (1996-1944)
Publication Type :
Academic Journal
Accession number :
177185208
Full Text :
https://doi.org/10.3390/ma17092166