  We consider the problem of designing a circularly polarized Fabry-P'erot
cavity (FPC) antenna for S-band sensing applications such as satellite
navigation and communication. The spatial distribution of peripheral roughness
of the unit cell of FPC's partially reflecting surface (PRS) serves as an
important design optimization criterion. However, the evaluation of each
candidate design using a full-wave solver is computationally expensive. To this
end, we propose a deep generative adversarial network (GAN) for realizing a
surrogate model that is trained with input-output pairs of antenna designs and
their corresponding patterns. Using the GAN framework, we quickly evaluate the
characteristics of a large volume of candidate designs and choose the antenna
design with an axial ratio of 0.4 dB, a gain of 7.5 dB, and a bandwidth of 269
MHz.
