Guest Editorial Machine Learning in Antenna Design, Modeling, and Measurements
Francesco P. Andriulli, Pai‐Yen Chen, Danilo Erricolo, Jian‐Ming Jin
Abstract
Machine learning (ML) is the study of computational methods for improving performance by mechanizing the acquisition of knowledge from experience. As a modern data-driven optimization and applied regression methodology, ML aims to provide increasing levels of automation in the knowledge engineering process, replacing much time-consuming human activity with automatic techniques that improve accuracy and/or efficiency by discovering and exploiting regularities in training data. Indeed, many ML and data-driven methods, such as conventional artificial neural networks (ANNs), were introduced and studied within electromagnetics a few decades ago. However, these past studies did not benefit from the most recent advances in ML, which have been driven by the present confluence of improved hardware performance at lower cost, advanced network algorithms and architectures, data science, and considerable efforts dedicated to advancing the computational electromagnetics (CEM) benchmark. Today, a broader family of ML techniques based on ANNs has been developed. Examples include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network, generative adversarial network, and deep reinforcement learning, which have been successfully applied to different engineering and science problems, ranging from image and video recognition, social media services, virtual personal assistant to autonomous vehicles, to name a few. This naturally suggests that applying ML to real-world electromagnetic problems could be one of the emerging trends in ML and artificial intelligence (AI) <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> – <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> . Indeed, ML has been becoming an important complement to existing experimental, computational, and theoretical aspects of electromagnetics.