Superscattering of Underwater Sound via Deep Learning Approach
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Abstract
We design a multilayer cylindrical structure to realize superscattering of underwater sound. Because of the near degeneracy of resonances in multiple channels of the structure, the scattering contributions from these resonances can overlap to break the single-channel limit of subwavelength objects. However, tuning the design parameters to achieve the target response is an optimization process that is tedious and time-consuming. Here, we demonstrate that a well-trained tandem neural network can deal with this problem efficiently, which can not only forwardly predict the scattering spectra of the multilayer structure with high precision, but also inversely design the required structural parameters efficiently.
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Cite this article:
Wenjie Miao, Zhiang Linghu, Qiujiao Du, Pai Peng, Fengming Liu. Superscattering of Underwater Sound via Deep Learning Approach[J]. Chin. Phys. Lett., 2023, 40(1): 014301. DOI: 10.1088/0256-307X/40/1/014301
Wenjie Miao, Zhiang Linghu, Qiujiao Du, Pai Peng, Fengming Liu. Superscattering of Underwater Sound via Deep Learning Approach[J]. Chin. Phys. Lett., 2023, 40(1): 014301. DOI: 10.1088/0256-307X/40/1/014301
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Wenjie Miao, Zhiang Linghu, Qiujiao Du, Pai Peng, Fengming Liu. Superscattering of Underwater Sound via Deep Learning Approach[J]. Chin. Phys. Lett., 2023, 40(1): 014301. DOI: 10.1088/0256-307X/40/1/014301
Wenjie Miao, Zhiang Linghu, Qiujiao Du, Pai Peng, Fengming Liu. Superscattering of Underwater Sound via Deep Learning Approach[J]. Chin. Phys. Lett., 2023, 40(1): 014301. DOI: 10.1088/0256-307X/40/1/014301
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