volume | PIER Journals

Abstract

A hybrid machine-learning-based optimization method is proposed for quick optimization of antenna shape design. The hybrid optimization method combines self-supervised learning and transfer learning. The application of self-supervised learning avoids the requirement to obtain labeled simulation data for electromagnetic samples, thereby reducing the difficulty of sample construction. The introduction of transfer learning further improves the sample utilization and optimizes efficiency in electromagnetic tasks. The proposed method enables rapid and high-degree-of-freedom optimization of antennas. To validate its effectiveness, a reflectarray antenna design incorporating distinct elements is employed as a case study. Simulation results indicate that the designed antenna exhibits a realized gain of 26.3 dBi and 46% aperture efficiency at the center frequency, and each element has a highly flexible independent structural design. During the optimization process, the proposed hybrid method demonstrates higher optimization efficiency than traditional methods, while significantly reducing sample construction time.

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Dr. Hao Huang

Dr. Xue-Song Yang