Developing a carpet cloak operating for a wide range of incident angles using a deep neural network and PSO algorithm
Designing invisibility cloaks has always been one of the most fascinating fields of research; in this regard, metasurface-based carpet cloaks have drawn researchers' attention due to their inherent tenuousness, resulting in a lower loss and easier fabrication. However, their performances are de...
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| Published in | Scientific reports Vol. 13; no. 1; pp. 670 - 11 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
12.01.2023
Nature Publishing Group Nature Portfolio |
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| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-023-27458-x |
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| Abstract | Designing invisibility cloaks has always been one of the most fascinating fields of research; in this regard, metasurface-based carpet cloaks have drawn researchers' attention due to their inherent tenuousness, resulting in a lower loss and easier fabrication. However, their performances are dependent on the incident angle of the coming wave; as a result, designing a carpet cloak capable of rendering objects under it invisible for a wide range of angles requires advanced methods. In this paper, using the Particle Swarm Optimization (PSO) algorithm, along with a trained neural network, a metasurface-based carpet cloak is developed capable to operate for a wide range of incident angles. The deep neural network is trained and used in order to accelerate the process of calculation of reflection phases provided by different unit cell designs. The resultant carpet cloak is numerically analyzed, and its response is presented and discussed. Both near-field and far-field results show that the designed carpet cloak operates very well for all incident angles in the range of 0 to 65 degrees. |
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| AbstractList | Abstract Designing invisibility cloaks has always been one of the most fascinating fields of research; in this regard, metasurface-based carpet cloaks have drawn researchers' attention due to their inherent tenuousness, resulting in a lower loss and easier fabrication. However, their performances are dependent on the incident angle of the coming wave; as a result, designing a carpet cloak capable of rendering objects under it invisible for a wide range of angles requires advanced methods. In this paper, using the Particle Swarm Optimization (PSO) algorithm, along with a trained neural network, a metasurface-based carpet cloak is developed capable to operate for a wide range of incident angles. The deep neural network is trained and used in order to accelerate the process of calculation of reflection phases provided by different unit cell designs. The resultant carpet cloak is numerically analyzed, and its response is presented and discussed. Both near-field and far-field results show that the designed carpet cloak operates very well for all incident angles in the range of 0 to 65 degrees. Designing invisibility cloaks has always been one of the most fascinating fields of research; in this regard, metasurface-based carpet cloaks have drawn researchers' attention due to their inherent tenuousness, resulting in a lower loss and easier fabrication. However, their performances are dependent on the incident angle of the coming wave; as a result, designing a carpet cloak capable of rendering objects under it invisible for a wide range of angles requires advanced methods. In this paper, using the Particle Swarm Optimization (PSO) algorithm, along with a trained neural network, a metasurface-based carpet cloak is developed capable to operate for a wide range of incident angles. The deep neural network is trained and used in order to accelerate the process of calculation of reflection phases provided by different unit cell designs. The resultant carpet cloak is numerically analyzed, and its response is presented and discussed. Both near-field and far-field results show that the designed carpet cloak operates very well for all incident angles in the range of 0 to 65 degrees. Designing invisibility cloaks has always been one of the most fascinating fields of research; in this regard, metasurface-based carpet cloaks have drawn researchers' attention due to their inherent tenuousness, resulting in a lower loss and easier fabrication. However, their performances are dependent on the incident angle of the coming wave; as a result, designing a carpet cloak capable of rendering objects under it invisible for a wide range of angles requires advanced methods. In this paper, using the Particle Swarm Optimization (PSO) algorithm, along with a trained neural network, a metasurface-based carpet cloak is developed capable to operate for a wide range of incident angles. The deep neural network is trained and used in order to accelerate the process of calculation of reflection phases provided by different unit cell designs. The resultant carpet cloak is numerically analyzed, and its response is presented and discussed. Both near-field and far-field results show that the designed carpet cloak operates very well for all incident angles in the range of 0 to 65 degrees.Designing invisibility cloaks has always been one of the most fascinating fields of research; in this regard, metasurface-based carpet cloaks have drawn researchers' attention due to their inherent tenuousness, resulting in a lower loss and easier fabrication. However, their performances are dependent on the incident angle of the coming wave; as a result, designing a carpet cloak capable of rendering objects under it invisible for a wide range of angles requires advanced methods. In this paper, using the Particle Swarm Optimization (PSO) algorithm, along with a trained neural network, a metasurface-based carpet cloak is developed capable to operate for a wide range of incident angles. The deep neural network is trained and used in order to accelerate the process of calculation of reflection phases provided by different unit cell designs. The resultant carpet cloak is numerically analyzed, and its response is presented and discussed. Both near-field and far-field results show that the designed carpet cloak operates very well for all incident angles in the range of 0 to 65 degrees. |
| ArticleNumber | 670 |
| Author | Kalhor, Ahmad Yousefi, Leila Fallah, Amirhossein |
| Author_xml | – sequence: 1 givenname: Amirhossein surname: Fallah fullname: Fallah, Amirhossein organization: School of Electrical and Computer Engineering, College of Engineering, University of Tehran – sequence: 2 givenname: Ahmad surname: Kalhor fullname: Kalhor, Ahmad organization: School of Electrical and Computer Engineering, College of Engineering, University of Tehran – sequence: 3 givenname: Leila surname: Yousefi fullname: Yousefi, Leila email: lyousefi@ut.ac.ir organization: School of Electrical and Computer Engineering, College of Engineering, University of Tehran |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36635479$$D View this record in MEDLINE/PubMed |
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| Snippet | Designing invisibility cloaks has always been one of the most fascinating fields of research; in this regard, metasurface-based carpet cloaks have drawn... Abstract Designing invisibility cloaks has always been one of the most fascinating fields of research; in this regard, metasurface-based carpet cloaks have... |
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| SubjectTerms | 639/166/987 639/766/25 Algorithms Computer Simulation Fabrication Floors and Floorcoverings Humanities and Social Sciences Light multidisciplinary Neural networks Neural Networks, Computer Refractometry - methods Scattering, Radiation Science Science (multidisciplinary) |
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| Title | Developing a carpet cloak operating for a wide range of incident angles using a deep neural network and PSO algorithm |
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