MetaPhyNet: intelligent design of large-scale metasurfaces based on physics-driven neural network
Metasurface has garnered extensive attention across multiple disciplines owing to its profound capability in electromagnetic (EM) manipulations. To determine its EM characteristics accurately, full-wave simulations are essential. These simulations necessitate significant amounts of time and memory r...
| প্রধান লেখক: | , , , , , , , , , |
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| বিন্যাস: | প্রবন্ধ |
| ভাষা: | English |
| প্রকাশিত: |
IOP Publishing
2024-01-01
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| মালা: | JPhys Photonics |
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | https://doi.org/10.1088/2515-7647/ad4cc8 |
| _version_ | 1831878185057255424 |
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| author | Jian Lin Su Jian Wei You Long Chen Xin Yi Yu Qing Chun Yin Guo Hang Yuan Si Qi Huang Qian Ma Jia Nan Zhang Tie Jun Cui |
| author_facet | Jian Lin Su Jian Wei You Long Chen Xin Yi Yu Qing Chun Yin Guo Hang Yuan Si Qi Huang Qian Ma Jia Nan Zhang Tie Jun Cui |
| author_sort | Jian Lin Su |
| collection | DOAJ |
| description | Metasurface has garnered extensive attention across multiple disciplines owing to its profound capability in electromagnetic (EM) manipulations. To determine its EM characteristics accurately, full-wave simulations are essential. These simulations necessitate significant amounts of time and memory resources, hindering the efficiency of the design process. In this article, we propose MetaPhyNet, a novel physics-driven neural network based on temporal coupled-mode theory (CMT) to address the challenges of low efficiency and high memory consumption in large-scale metasurface design. In the proposed approach, a surrogate model is developed to achieve rapid prediction of the EM responses of ultra-large-scale metasurfaces. In comparison with the full-wave EM simulation, the proposed model reduces the simulation time of the ultra-large-scale metasurface by up to two orders of magnitude and the memory consumption by more than two orders of magnitude. Our proposed approach aims to enhance the efficiency and intelligence in metasurface design by leveraging the principles of CMT within a neural network framework. Through this innovative integration of physics-based modeling and machine learning, we seek to achieve significant advancements in the design efficiency of metasurfaces. We apply the proposed model to optimize the design of two metasurface absorbers to showcase the effectiveness of our proposed approach. Simulations and experimental results are provided to demonstrate the value and impact of our approach in addressing existing challenges in full-wave EM simulation-based design optimizations of metasurfaces. |
| first_indexed | 2025-03-21T21:27:24Z |
| format | Article |
| id | doaj.art-3f34a23b94e6451e8adf5296f0daf8af |
| institution | Directory Open Access Journal |
| issn | 2515-7647 |
| language | English |
| last_indexed | 2025-03-21T21:27:24Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | JPhys Photonics |
| spelling | doaj.art-3f34a23b94e6451e8adf5296f0daf8af2024-05-28T10:17:44ZengIOP PublishingJPhys Photonics2515-76472024-01-016303501010.1088/2515-7647/ad4cc8MetaPhyNet: intelligent design of large-scale metasurfaces based on physics-driven neural networkJian Lin Su0Jian Wei You1https://orcid.org/0009-0006-4977-7461Long Chen2Xin Yi Yu3Qing Chun Yin4Guo Hang Yuan5Si Qi Huang6Qian Ma7https://orcid.org/0000-0003-3078-2624Jia Nan Zhang8Tie Jun Cui9https://orcid.org/0000-0002-5862-1497State Key Laboratory of Millimeter Waves, Southeast University , Nanjing 210096, People’s Republic of China; School of Information Science and Engineering, Southeast University , Nanjing 210096, People’s Republic of ChinaState Key Laboratory of Millimeter Waves, Southeast University , Nanjing 210096, People’s Republic of China; School of Information Science and Engineering, Southeast University , Nanjing 210096, People’s Republic of China; Institute of Electromagnetic Space, Southeast University , Nanjing 210096, People’s Republic of ChinaState Key Laboratory of Millimeter Waves, Southeast University , Nanjing 210096, People’s Republic of China; School of Information Science and Engineering, Southeast University , Nanjing 210096, People’s Republic of ChinaState Key Laboratory of Millimeter Waves, Southeast University , Nanjing 210096, People’s Republic of China; School of Information Science and Engineering, Southeast University , Nanjing 210096, People’s Republic of ChinaSchool of Information Science and Engineering, Southeast University , Nanjing 210096, People’s Republic of ChinaSchool of Information Science and Engineering, Southeast University , Nanjing 210096, People’s Republic of ChinaState Key Laboratory of Millimeter Waves, Southeast University , Nanjing 210096, People’s Republic of China; School of Information Science and Engineering, Southeast University , Nanjing 210096, People’s Republic of ChinaState Key Laboratory of Millimeter Waves, Southeast University , Nanjing 210096, People’s Republic of China; School of Information Science and Engineering, Southeast University , Nanjing 210096, People’s Republic of China; Institute of Electromagnetic Space, Southeast University , Nanjing 210096, People’s Republic of ChinaState Key Laboratory of Millimeter Waves, Southeast University , Nanjing 210096, People’s Republic of China; School of Information Science and Engineering, Southeast University , Nanjing 210096, People’s Republic of China; Institute of Electromagnetic Space, Southeast University , Nanjing 210096, People’s Republic of ChinaState Key Laboratory of Millimeter Waves, Southeast University , Nanjing 210096, People’s Republic of China; School of Information Science and Engineering, Southeast University , Nanjing 210096, People’s Republic of China; Institute of Electromagnetic Space, Southeast University , Nanjing 210096, People’s Republic of ChinaMetasurface has garnered extensive attention across multiple disciplines owing to its profound capability in electromagnetic (EM) manipulations. To determine its EM characteristics accurately, full-wave simulations are essential. These simulations necessitate significant amounts of time and memory resources, hindering the efficiency of the design process. In this article, we propose MetaPhyNet, a novel physics-driven neural network based on temporal coupled-mode theory (CMT) to address the challenges of low efficiency and high memory consumption in large-scale metasurface design. In the proposed approach, a surrogate model is developed to achieve rapid prediction of the EM responses of ultra-large-scale metasurfaces. In comparison with the full-wave EM simulation, the proposed model reduces the simulation time of the ultra-large-scale metasurface by up to two orders of magnitude and the memory consumption by more than two orders of magnitude. Our proposed approach aims to enhance the efficiency and intelligence in metasurface design by leveraging the principles of CMT within a neural network framework. Through this innovative integration of physics-based modeling and machine learning, we seek to achieve significant advancements in the design efficiency of metasurfaces. We apply the proposed model to optimize the design of two metasurface absorbers to showcase the effectiveness of our proposed approach. Simulations and experimental results are provided to demonstrate the value and impact of our approach in addressing existing challenges in full-wave EM simulation-based design optimizations of metasurfaces.https://doi.org/10.1088/2515-7647/ad4cc8metasurfacesintelligent designphysics-driven neural networktemporal coupled-mode theory |
| spellingShingle | Jian Lin Su Jian Wei You Long Chen Xin Yi Yu Qing Chun Yin Guo Hang Yuan Si Qi Huang Qian Ma Jia Nan Zhang Tie Jun Cui MetaPhyNet: intelligent design of large-scale metasurfaces based on physics-driven neural network JPhys Photonics metasurfaces intelligent design physics-driven neural network temporal coupled-mode theory |
| title | MetaPhyNet: intelligent design of large-scale metasurfaces based on physics-driven neural network |
| title_full | MetaPhyNet: intelligent design of large-scale metasurfaces based on physics-driven neural network |
| title_fullStr | MetaPhyNet: intelligent design of large-scale metasurfaces based on physics-driven neural network |
| title_full_unstemmed | MetaPhyNet: intelligent design of large-scale metasurfaces based on physics-driven neural network |
| title_short | MetaPhyNet: intelligent design of large-scale metasurfaces based on physics-driven neural network |
| title_sort | metaphynet intelligent design of large scale metasurfaces based on physics driven neural network |
| topic | metasurfaces intelligent design physics-driven neural network temporal coupled-mode theory |
| url | https://doi.org/10.1088/2515-7647/ad4cc8 |
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