![]() ![]() This work proposes an alternative approach with the unit element representations constructed using deep learning with automated adjustment of the model architecture. Unfortunately, conventional modeling methods require large numbers of training data samples to render accurate surrogates, which turns detrimental to the optimization process efficiency. A practical solution is surrogate-assisted design with the metamodels constructed for the RA unit elements. In particular, RA optimization is extremely expensive when conducted at the level of EM simulation models, otherwise necessary to ensure reliability. In either case, a large number of variables (induced by the need for independent adjustment of individual unit cell geometries), and the necessity of handling several requirements, make the design process of reflectarrays a challenging endeavor. RAs based on grounded dielectric layers offer improved performance and flexibility in terms of shaping the phase reflection response. ![]() ![]() Notwithstanding, available microstrip implementations are inherently narrow-band, and heavily affected by conductor and surface wave losses. These characteristics make them attractive solutions, among others, for satellite communications or mobile radar antennas. Reflectarrays (RAs) have been attracting considerable interest in the recent years due to their appealing features, in particular, a possibility of realizing pencil-beam radiation patterns, as in the phased arrays, but without the necessity of incorporating the feeding networks. ![]()
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