Rapid Prediction of EM Scattering from Finite Periodic Structures Using CBFM with ANNs
In this article, the artificial neural networks (ANNs) are combined with the characteristic basis function method (CBFM) to fast predict the electromagnetic scattering from finite periodic structures. The amplitude and the phase of the expansion coefficients of the characteristic basis functions (CBFs) are predicted by two different ANNs. Then, the predicted expansion coefficients are used to calculated the radar cross sections (RCSs) of the finite periodic structures. Numerical results are given to show the performance of the ANN-assisted CBFM.
Authors
- Wu Yang (ORCID: https://orcid.org/0000-0003-2788-2608)
- Fan Gao (ORCID: https://orcid.org/0000-0003-1481-4374)
- Weibing Lu (ORCID: https://orcid.org/0000-0001-6337-1739)
- Xinlei Chen (ORCID: https://orcid.org/0000-0003-3570-588X)
- Hao Meng (ORCID: https://orcid.org/0000-0003-3380-3179)
- Mingxin Wang (ORCID: https://orcid.org/0000-0001-7781-6751)
Institutions
- State Key Laboratory of Millimeter Waves
- Southeast University (CN)
- Nanjing University of Aeronautics and Astronautics (CN)
Publication Details
- Published
- 2024-09-22
- DOI
- https://doi.org/10.1109/apcap62011.2024.10880996
- Citations
- 1
- Primary Topic
- Electromagnetic Scattering and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.34
Funders
- National Natural Science Foundation of China
- Ministry of Education
- Nanjing University
- Southeast University
- State Key Laboratory of Millimeter Waves
- Nature