Optimizing Microstrip Patch Antenna Performance: Leveraging Machine Learning for Comparative Analysis of Antenna Di-mensions and Slot Structures

Mona K. ElAbbasi, Mervat Madi, Karim Y. Kabalan, Jihan Salah,
Optimizing Microstrip Patch Antenna Performance: Leveraging Machine Learning for Comparative Analysis of Antenna Di-mensions and Slot Structures,
Results in Engineering,
2025,
107523,
ISSN 2590-1230,
https://doi.org/10.1016/j.rineng.2025.107523.
(https://www.sciencedirect.com/science/article/pii/S2590123025035789)
Abstract: Microstrip patch antennas (MPAs) are widely used in wireless systems due to their compact size and ease of fabrication, but their design still relies on time-consuming full-wave electromagnetic simulations. This study presents a unified machine learning (ML)-assisted pipeline for predicting and optimizing antenna performance across three common MPA shapes—rectangular, circular, and triangular—with various slot configurations and substrate types. Using a curated dataset of 1,500 designs, we apply support vector regression (SVR), which models nonlinear relationships using kernel functions; Gaussian process regression (GPR), which provides both predictions and uncertainty estimates; and artificial neural networks (ANN), which learn complex patterns from data. For classification tasks, we introduce a lightweight convolutional neural network (CNN) to pre-screen slot regimes. We also propose a smart simulation strategy that uses model uncertainty to trigger new simulations only when needed, reducing computational cost. Validation includes parity plots, residual histograms, and S11 overlays, while sensitivity analysis highlights the most influential geometric and material features. The pipeline offers interpretable, simulation-efficient design guidance and lays the foundation for future hardware validation and multi-objective optimization.
Keywords: Microstrip patch antenna; machine learning; surrogate modeling; inverse design; support vector regression (SVR); Gaussian process regression (GPR); artificial neural networks (ANN); convolutional neural network (CNN); sensitivity analysis; electromagnetic simulation