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Study of Machine Learning Algorithms for Output Response Prediction of an UWB Fractal Antenna

In this paper, we showcase the efficiency of machine learning (ML) algorithms to predict fractal antenna output response metrics. Radial basis function and least squared regression algorithms are trained and validated to understand the generalization capabilities. The complete process is carried out using Altair Hyperstudy, and the results are compared against the output response of the same fractal antenna simulated using Altair FEKO. A well-trained ML model can predict the antenna output response 160 times faster than a traditional electromagnetic solver.