# Application of Machine Learning in Electromagnetics: Mini-Review
## 3 ML and EM
### Design Optimization and Synthesis  
Antennae are an integral part of the EM system, which starts with the design, implementation, and testing [35]. 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. Thus, applying ML models in the design of compact antenna or antenna arrays with high gain, transmission efficiency, and directivity, including suitable material selection, can enrich the effectiveness of the design. ML has been used in different applications of antenna design and optimization [37; 38; 39]. [PERSON] et al. [40] proposed an ML framework that enabled a simulation-based optimization of thinned arrays, considering network-level metrics such as signal to interference plus noise ratio statistics, based on a Monte Carlo approach. [PERSON] et al. [41] proposed a multi-objective optimization-based sequential mode algorithm that provided optimized design characteristics of antennas using a few hundred full-wave EM simulations. Surrogate-modeling-based antenna optimization is one of the most important methods in antenna design [42]. Its purpose is to replace computationally expensive EM simulation with computationally cheap estimation models. Surrogate models are formed using statistical learning techniques [43]. A multistage collaborative ML platform (MS-CoML) was introduced in another study [44], which reported almost double speed in antenna modelling without compromising the accuracy via single-output Gaussian process regression (SOGPR). By jointly applying three ML methods i.e., SOGPR and symmetric and asymmetric multioutput GPR methods, the surrogate antenna models for different design objectives were constructed based on a limited number of high-fidelity responses, while achieving high prediction accuracy. Three antenna examples, i.e., single-band, broadband, and multiband designs operating at the Q, V, and S/C bands, were chosen to validate the proposed method. Simulation results showed that the MS-CoML method can greatly minimize the total optimization time without compromising modeling accuracy or performance. An advanced study of a modified parallel simulation surrogate model-assisted system (PSADEA) was proposed previously [45] that showed 1.8 times faster performance in optimization than the conventional surrogate modelling method with higher antenna quality. Another approach, which claimed 90% more time saved in antenna optimization compared to other surrogate models, is the single-fidelity, EM-model-based, training cost-reduced, surrogate model-assisted hybrid differential evolution for complex antenna optimization (TR-SADEA) [46]. It is a self-adaptive hybrid surrogate modeling framework that facilitates better performance for complex antenna design with increased design variables and specifications. More than 80% computational expense reduction was made possible by [47] implementing a Gaussian process regression (GPR) model when the training data contain high fidelity. A semi-supervised approach-based process was proposed in another study [48] where a GP model and an SVM model were concurrently trained using a small amount of pre-labeled samples. The system was controllable by selecting the required accuracy, which optimizes design time. It offered high-precision predictive ability with respect to conventional supervised learning-based surrogate modeling using less labeled data and provided quality antenna designs utilizing only 10 to 15 EM simulations. [PERSON] et al. [49] developed a hybrid ML model where ten different models were initially trained using a small set of data, which worked as a base learner.