Guest Editorial Machine Learning in Antenna Design, Modeling, and Measurements

Machine learning (ML) is the study of computational methods for improving performance by mechanizing acquisition knowledge from experience. As a modern data-driven optimization and applied regression methodology, ML aims to provide increasing levels automation in engineering process, replacing much time-consuming human activity with automatic techniques that improve accuracy and/or efficiency discovering exploiting regularities training data. Indeed, many methods, such as conventional artificial neural networks (ANNs), were introduced studied within electromagnetics few decades ago. However, these past studies did not benefit most recent advances ML, which have been driven present confluence improved hardware at lower cost, advanced network algorithms architectures, data science, considerable efforts dedicated advancing (CEM) benchmark. Today, broader family based on ANNs has developed. Examples include deep (DNN), convolutional (CNN), recurrent network, generative adversarial reinforcement learning, successfully different science problems, ranging image video recognition, social media services, virtual personal assistant autonomous vehicles, name few. This naturally suggests applying real-world electromagnetic problems could be one emerging trends intelligence (AI) [1] – rid="ref2" xmlns:xlink="http://www.w3.org/1999/xlink"/> rid="ref3" rid="ref4" xmlns:xlink="http://www.w3.org/1999/xlink">[4] . becoming an important complement existing experimental, computational, theoretical aspects electromagnetics.