volume | PIER Journals

Abstract

Conventional full-wave methods face prohibitive computational costs for far-field scattering optimization of metasurface-integrated cavity structures. To address this limitation, a lightweight residual neural network is introduced within a two-stage scattering prediction framework. This framework effectively mitigates model degradation. The first stage employs shallow convolutional networks to extract local phase-coupling features. The second stage integrates residual layers with fully connected layers to refine cross-scale scattering responses. A compact CNN-ResNet surrogate model is developed for rapid cavity scattering prediction. With only 2.5×10⁴ parameters and training on 500 full-wave samples spanning 6.0-16.0 GHz, the model achieves high computational efficiency. The proposed approach directly maps binary phase-coded matrices to far-field electromagnetic characteristics. Extensive validation on a cavity structure across 6.0-16.0 GHz demonstrates excellent accuracy. The per-sample runtime is reduced from hours to milliseconds while maintaining prediction errors below 3 dB. These results confirm the effectiveness of the approach in enabling fast and accurate electromagnetic scattering prediction for complex cavity environments. The approach provides a practical solution for metasurface-integrated cavity optimization.

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Dr. Xi Liu

Dr. Peng Nian

Dr. Yu Zhang

Professor Yi Ren

Dr. Yi-Xin Guo

Dr. Yang-Chun Gao

Dr. Bing Chen