Transfer Learning Based Microstrip Antenna Design

This paper presents a computationally efficient data-driven technique for designing multi-band microstrip antennas. The proposed methodology involves a two-step process. First, a pre-trained model, trained on extensive data on air-filled microstrip antennas, is used for knowledge transfer. This pre-trained model is fine-tuned with a limited set of dielectric simulations, reducing data acquisition costs. In the second step, the developed forward model serves as a surrogate to design dielectric-filled antennas using the Modified Binary Particle Swarm Optimization (MBPSO) algorithm. In contrast to conventional methods, this approach enables the design of compact antennas across various dielectrics and frequency ranges, with a significantly reduced number of time-consuming dielectric simulations (86 % fewer simulations).