Metagrating-based acoustic wavelength division multiplexing enabled by deterministic and probabilistic deep learning models
Wavelength division multiplexing (WDM), the technology that utilizes different wavelengths of carrier signal as independent information channels and helps to realize enhanced information density and functional diversity, is desired in various acoustic applications for the growing demand of higher tr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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American Physical Society
2022-08-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.4.033165 |
| _version_ | 1797210658970796032 |
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| author | Zhanhang Du Jun Mei |
| author_facet | Zhanhang Du Jun Mei |
| author_sort | Zhanhang Du |
| collection | DOAJ |
| description | Wavelength division multiplexing (WDM), the technology that utilizes different wavelengths of carrier signal as independent information channels and helps to realize enhanced information density and functional diversity, is desired in various acoustic applications for the growing demand of higher transmission efficiency and lower cost. In this paper, we marry the rising concept of acoustic metagrating with the technology of WDM and demonstrate that interesting and distinct wavelength-dependent functionalities with unitary efficiency in prespecified frequency ranges can be realized by a simple grating structure. To this end, we integrate the particle swarm optimization with the deep learning technique to resolve the uncertainty issue and multi-objective problem and develop both deterministic and probabilistic neural networks for the inverse design of a metagrating-based WDM device. As a result, the computational cost is greatly reduced, and the probabilistic model reveals the sensitivity and flexibility of the parameters of the metagrating with respect to the desired functionality. In this paper, we not only provide an intelligent inverse design paradigm of high-performance WDM devices for multiple manipulation purposes but also present a feasible solution for the development of integrated acoustic devices for wavelength-dependent applications. |
| first_indexed | 2024-04-24T10:14:06Z |
| format | Article |
| id | doaj.art-8eb964b403304c5daff1f77b7faab926 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:14:06Z |
| publishDate | 2022-08-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-8eb964b403304c5daff1f77b7faab9262024-04-12T17:24:08ZengAmerican Physical SocietyPhysical Review Research2643-15642022-08-014303316510.1103/PhysRevResearch.4.033165Metagrating-based acoustic wavelength division multiplexing enabled by deterministic and probabilistic deep learning modelsZhanhang DuJun MeiWavelength division multiplexing (WDM), the technology that utilizes different wavelengths of carrier signal as independent information channels and helps to realize enhanced information density and functional diversity, is desired in various acoustic applications for the growing demand of higher transmission efficiency and lower cost. In this paper, we marry the rising concept of acoustic metagrating with the technology of WDM and demonstrate that interesting and distinct wavelength-dependent functionalities with unitary efficiency in prespecified frequency ranges can be realized by a simple grating structure. To this end, we integrate the particle swarm optimization with the deep learning technique to resolve the uncertainty issue and multi-objective problem and develop both deterministic and probabilistic neural networks for the inverse design of a metagrating-based WDM device. As a result, the computational cost is greatly reduced, and the probabilistic model reveals the sensitivity and flexibility of the parameters of the metagrating with respect to the desired functionality. In this paper, we not only provide an intelligent inverse design paradigm of high-performance WDM devices for multiple manipulation purposes but also present a feasible solution for the development of integrated acoustic devices for wavelength-dependent applications.http://doi.org/10.1103/PhysRevResearch.4.033165 |
| spellingShingle | Zhanhang Du Jun Mei Metagrating-based acoustic wavelength division multiplexing enabled by deterministic and probabilistic deep learning models Physical Review Research |
| title | Metagrating-based acoustic wavelength division multiplexing enabled by deterministic and probabilistic deep learning models |
| title_full | Metagrating-based acoustic wavelength division multiplexing enabled by deterministic and probabilistic deep learning models |
| title_fullStr | Metagrating-based acoustic wavelength division multiplexing enabled by deterministic and probabilistic deep learning models |
| title_full_unstemmed | Metagrating-based acoustic wavelength division multiplexing enabled by deterministic and probabilistic deep learning models |
| title_short | Metagrating-based acoustic wavelength division multiplexing enabled by deterministic and probabilistic deep learning models |
| title_sort | metagrating based acoustic wavelength division multiplexing enabled by deterministic and probabilistic deep learning models |
| url | http://doi.org/10.1103/PhysRevResearch.4.033165 |
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