Prediction Network of Metamaterial with Split Ring Resonator Based on Deep Learning
Abstract The introduction of “metamaterials” has had a profound impact on several fields, including electromagnetics. Designing a metamaterial’s structure on demand, however, is still an extremely time-consuming process. As an efficient machine learning method, deep learning has been widely used for...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-04-01
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| Series: | Nanoscale Research Letters |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s11671-020-03319-8 |
| _version_ | 1797723848543567872 |
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| author | Zheyu Hou Tingting Tang Jian Shen Chaoyang Li Fuyu Li |
| author_facet | Zheyu Hou Tingting Tang Jian Shen Chaoyang Li Fuyu Li |
| author_sort | Zheyu Hou |
| collection | DOAJ |
| description | Abstract The introduction of “metamaterials” has had a profound impact on several fields, including electromagnetics. Designing a metamaterial’s structure on demand, however, is still an extremely time-consuming process. As an efficient machine learning method, deep learning has been widely used for data classification and regression in recent years and in fact shown good generalization performance. We have built a deep neural network for on-demand design. With the required reflectance as input, the parameters of the structure are automatically calculated and then output to achieve the purpose of designing on demand. Our network has achieved low mean square errors (MSE), with MSE of 0.005 on both the training and test sets. The results indicate that using deep learning to train the data, the trained model can more accurately guide the design of the structure, thereby speeding up the design process. Compared with the traditional design process, using deep learning to guide the design of metamaterials can achieve faster, more accurate, and more convenient purposes. |
| first_indexed | 2024-03-12T10:07:28Z |
| format | Article |
| id | doaj.art-809b29ef50d942d1864fdc6a08c5234c |
| institution | Directory Open Access Journal |
| issn | 1556-276X |
| language | English |
| last_indexed | 2024-03-12T10:07:28Z |
| publishDate | 2020-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Nanoscale Research Letters |
| spelling | doaj.art-809b29ef50d942d1864fdc6a08c5234c2023-09-02T11:06:12ZengSpringerOpenNanoscale Research Letters1556-276X2020-04-011511810.1186/s11671-020-03319-8Prediction Network of Metamaterial with Split Ring Resonator Based on Deep LearningZheyu Hou0Tingting Tang1Jian Shen2Chaoyang Li3Fuyu Li4Hainan UniversityChengdu University of Information TechnologyHainan UniversityHainan UniversityChengdu University of Information TechnologyAbstract The introduction of “metamaterials” has had a profound impact on several fields, including electromagnetics. Designing a metamaterial’s structure on demand, however, is still an extremely time-consuming process. As an efficient machine learning method, deep learning has been widely used for data classification and regression in recent years and in fact shown good generalization performance. We have built a deep neural network for on-demand design. With the required reflectance as input, the parameters of the structure are automatically calculated and then output to achieve the purpose of designing on demand. Our network has achieved low mean square errors (MSE), with MSE of 0.005 on both the training and test sets. The results indicate that using deep learning to train the data, the trained model can more accurately guide the design of the structure, thereby speeding up the design process. Compared with the traditional design process, using deep learning to guide the design of metamaterials can achieve faster, more accurate, and more convenient purposes.http://link.springer.com/article/10.1186/s11671-020-03319-8Deep learningSplit ring resonatorMetamaterial |
| spellingShingle | Zheyu Hou Tingting Tang Jian Shen Chaoyang Li Fuyu Li Prediction Network of Metamaterial with Split Ring Resonator Based on Deep Learning Nanoscale Research Letters Deep learning Split ring resonator Metamaterial |
| title | Prediction Network of Metamaterial with Split Ring Resonator Based on Deep Learning |
| title_full | Prediction Network of Metamaterial with Split Ring Resonator Based on Deep Learning |
| title_fullStr | Prediction Network of Metamaterial with Split Ring Resonator Based on Deep Learning |
| title_full_unstemmed | Prediction Network of Metamaterial with Split Ring Resonator Based on Deep Learning |
| title_short | Prediction Network of Metamaterial with Split Ring Resonator Based on Deep Learning |
| title_sort | prediction network of metamaterial with split ring resonator based on deep learning |
| topic | Deep learning Split ring resonator Metamaterial |
| url | http://link.springer.com/article/10.1186/s11671-020-03319-8 |
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