# Application of Machine Learning in Electromagnetics: Mini-Review
## 1 Introduction  
Recent development in the field of electromagnetics (EM) and its diverse applications have attracted a wider community of researchers [1]. Among the different aspects of EM, antenna research has evolved and transformed in totality from the design to end-use application [2, 3, 4, 5, 6]. Advanced network systems [7, 8], implantable devices [9, 10], wearables [11], flexible devices [12, 13], textile products [14], and modern control system development [15, 16] necessitate futuristic antenna technology and performance requirements. For example, with the advent of the 5G spectrum, antenna design is by far the most challenging part of the implementation as it is entirely dependent on the end device form factor. This inevitably pushes antenna design for 5G devices to fit the ever-increasing requirements for greater bandwidth, more frequency bands, and superior interference immunity [17, 18]. Furthermore, fault detection in antenna arrays and inverse scattering-based non-linear problems need sophisticated yet cost-effective solutions, where Machine Learning (ML) can provide an edge over other techniques [19, 20].  
ML techniques have been extensively used in engineering and medical fields. They have the ability to discover specific trends and patterns within large volumes of complex datasets that would not be apparent to humans. As ML algorithms gain experience, they continue improving in accuracy and efficiency. In addition, ML algorithms are good at handling data that are multi-dimensional and multi-variety. Due to these unique features, ML is being used in classification and optimization problems in the EM domain, specifically antennas.  
As modern discoveries in the field of wireless devices are in the upswing, versatile and complex designs of antennas have been surfacing. With the increased complexities, it is important to minimize design time and overhead costs to achieve optimized antenna implementation. ML has contributed to parameter optimization and evolutionary algorithm development in antenna research [21; 22]. For instance, the usual synthesis mechanism utilizing a full-wave electromagnetic simulator is gradually being replaced by faster and more cost-effective ML algorithms. For its unparalleled performance in unforeseen situations encompassing non-linear environments, researchers are utilizing it to solve complex and modern conundrums with ease. ML has simplified the maintenance and control of fabrication systems to achieve significant miniaturization [23]. Various automated and cost-effective design procedures using ML techniques have been used to develop complex antenna technologies [24; 25]. Moreover, different fault detection in large antenna-array-based knotty systems have also been resolved by ML.  
This paper explores different ML applications from the perspective of EM, especially antenna design, synthesis, manufacturing, development, and the detection of anomalies. Moreover, this article describes different ML algorithms, optimization procedures, usability, and selection of the best-suited model for the specific application. Moreover, the ML applications in different domains of antennae are clustered for ease in comprehension. Finally, the challenges and limitations of ML applications in EM have been discussed. This review is divided into six sections. Section 1 provides the introduction, Section 2 includes necessary background information to understand ML, Section 3 describes different ML applications in the EM field, Section 4 discusses the limitations of ML algorithms, Section 5 suggests future scopes, and Section 6 concludes the article with necessary explanations supporting this innovative research area.