# Application of Machine Learning in Electromagnetics: Mini-Review
## 3 ML and EM
### Design Optimization and Synthesis
The initial predictions were then inserted into a K-nearest neighbors (KNN) model, which reported the final prediction of 0.00456 mean squared error in the case of a triangular-shaped pin-fed patch antenna. To increase the transmission efficiency of antennas used in wireless power transmission, ML has become a reliable assistive technology [50]. Antenna synthesis involves the determination of a geometrical or physical form from the knowledge of the electrical parameters. ML models can be fully used to enhance antenna synthesis efficiency. Gaussian Boosting Tree (GBT) was applied in a previous study [51] to synthesize a phase array antenna by estimating the phase angle for different amplitudes. To predict the phase shift in a reflect array antenna, another study [52] showed significant accuracy using a deep CNN-based AlexNet. The model takes the radiation pattern and beam direction as inputs and predicts the phase shift with less than 0.4% prediction error. Another ML approach was introduced in [53] where the coupling coefficient between pantograph arcing and a GSM-R antenna is determined utilizing multiple neural network (NN) regressors. The study showed significantly faster performance (almost three times faster via ML) while conserving the accuracy of the coupling coefficient. NN has provided over 99% accuracy towards [54] synthesizing an H-shaped rectangular microstrip antenna (RMSA). However, studies have shown that the Radial Basis Functions (RBF) of ANN are best suitable for evaluating the resonant frequency of rectangular microstrip patch antennae [55]. Radiation field estimation utilizing the near-field focusing technique offered by an earlier report [56] showed faster performance of the SVM model with a smaller training dataset. Investigators [57] developed an inverse NN (INN) model that could determine the VSWR, gain, radiation pattern, and radiation efficiency of different planes with greater accuracy using a small dataset for training, with only 36 samples. INN has also shown usability in transmit array antenna synthesis from a given transmission co-efficient [58]. [PERSON] et al. [59] showed how modern ML algorithms such as least absolute shrinkage and selection operator (lasso), KNN, and ANN improved the design optimization and synthesis of antennae based on a specific bandwidth selection. The performance table and the frequency response curve for the above study are shown in Figure 4, which suggests the KNN algorithm as being the fastest among all. Again, in a different study [60], a modified KNN showed the lowest S\({}_{11}\) parameter compared to GPR, ANN, and a conjugate gradient (CG) with a small dataset of 36 samples. CNN was used earlier [61] to estimate the phase angle from an input (two-dimensional radiation pattern). The network was able to accurately calculate phases to synthesize the desired pattern. Figure 4: (**a**) Comparison of different ML algorithms based on computational time and figure of merit for different ML techniques, and (**b**) comparison of S\({}_{11}\) parameter predicted by ML techniques with respect to the reference design parameters [59]. An adaptive chaotic particle swarm optimization (PSO) algorithm was used to avoid trapping in the local optimum, resulting in premature convergence of the antenna synthesis. A prior article [62] provided a DL methodology that determined the amplitude and phase of the antenna elements in response to the input radiation patterns, but for 0\({}^{\circ}\) and 360\({}^{\circ}\), it will show less accuracy. DL-based optimizers also provide significant improvements in DL models [63]. A combined GP CNN model was proposed by [PERSON] et al. [64] as a tool for the rapid optimization of EM problems such as antenna design. PSO was also used to optimize the model parameter, which elevated the performance, reduced convergence time, and increased the design accuracy. For higher bandwidth and gain, PSO was used prior [65] for optimizing a dielectric resonator antenna.