Haoda Xia
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Haoda Xia has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Ning Xu
School of Information Engineering, Wuhan University of Technology, Wuhan, China
1 shared publication - Quanshi Qin
School of Physics and Mechanics, Wuhan University of Technology, Wuhan 430070, China
1 shared publication - Shi Pu
School of Physics and Mechanics, Wuhan University of Technology, Wuhan 430070, China
1 shared publication - Shuang Zeng
International School of Materials Science and Engineering (School of Materials and Microelectronics), Wuhan University of Technology, Wuhan 430070, China
1 shared publication
Latest Publications
- Open Access
- 28 January 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (3) , 1320
Inverse Design of Chessboard Metasurface for Broadband Monostatic RCS Reduction Based on CNN-KAN with Attention Mechanism
An efficient deep-learning-based framework for optimization-based inverse design of electromagnetic metasurface design is proposed in this paper. A novel unit-cell parameterization strategy generates 16-element structures via symmetry operations governed by ten geometric parameters, overcoming the inefficiencies of pixel-based representations. A dataset of 16,000 parameter–reflection phase pairs is constructed, and a hybrid model combining Convolutional Neural Network (CNN), attention mechanisms, and the Kolmogorov–Arnold Network (KAN) is developed for broadband response prediction. The coefficient of determination (R2) of the proposed model is 0.8837 in the 2–18 GHz band, which is 44.87% higher than the R2 without KAN. The proposed chessboard metasurface achieves a 10 dB monostatic radar cross-section (RCS) reduction under normal incidence over a wide frequency band from 7.4 to 15.2 GHz, corresponding to a relative bandwidth of 69%. This approach provides a generalizable, data-efficient solution for intelligent metasurface design.
- 28 January 2026
- Published by MDPI AG in Applied Sciences
- Vol. 16 (3) , 1320
Open AccessInverse Design of Chessboard Metasurface for Broadband Monostatic RCS Reduction Based on CNN-KAN with Attention Mechanism
An efficient deep-learning-based framework for optimization-based inverse design of electromagnetic metasurface design is proposed in this paper. A novel unit-cell parameterization strategy generates 16-element structures via symmetry operations governed by ten geometric parameters, overcoming the inefficiencies of pixel-based representations. A dataset of 16,000 parameter–reflection phase pairs is constructed, and a hybrid model combining Convolutional Neural Network (CNN), attention mechanisms, and the Kolmogorov–Arnold Network (KAN) is developed for broadband response prediction. The coefficient of determination (R2) of the proposed model is 0.8837 in the 2–18 GHz band, which is 44.87% higher than the R2 without KAN. The proposed chessboard metasurface achieves a 10 dB monostatic radar cross-section (RCS) reduction under normal incidence over a wide frequency band from 7.4 to 15.2 GHz, corresponding to a relative bandwidth of 69%. This approach provides a generalizable, data-efficient solution for intelligent metasurface design.