Machine Learning-assisted Antenna Design optimization: A Review and the State-of-the-art

Abstract

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Representation Learning-Driven Fully Automated Framework for the Inverse Design of Frequency-Selective Surfaces

“…Similarly, we pre-defined the constraints as a high-pass behavior, with a cut-off frequency at 7.5 GHz, an insert loss of less than 4 dB, and a roll-off rate of 15 dB/GHz, as expressed in (26):…”

“…Machine learning-based methods are popular due to their unique merits compared to optimization algorithmbased methods, such as genetic algorithm-based methods (GA) [23]- [25]. A number of full-wave simulations are inevitable for optimization methods [26]. By contrast, machine learningbased methods accumulate intelligence from historical data to form a surrogate model.…”

Application of Machine Learning in Electromagnetics: Mini-Review

“…Traditionally, full wave (FW) EM simulation, such as finite-difference time-domain (FDTD) or finite-element modeling (FEM) methods, is used for antenna design and optimization. These methods require large computational resources and time [36]. In fact, optimizing antenna arrays may involve significant repetitions of EM simulations to fine-tune the geometric and/or material parameters for performance improvement.…”

Antenna S-parameter optimization based on golden sine mechanism based honey badger algorithm with tent chaos

“…In contrast, global optimization methods seem to attract more interest because they are robust and require no significant modifications during their design or implementation. However, they usually necessitate a significant number of electromagnetic simulations, which can be costly, to obtain optimal design parameters [ 35 ]. A particularly efficient alternative to conventional methods for resolving these issues is metaheuristic optimization.…”