PatchData: A Dataset of Pixelated Patch Antenna Topologies and Simulated Radiation Patterns for Deep Learning-Driven Optimizations

PatchData is an open-access dataset of pixelated microstrip patch antenna topologies and their corresponding simulated electromagnetic responses, developed to support data-driven antenna design, topology optimization, surrogate modeling, and deep learning applications. The dataset contains 2,398 simulated antenna configurations generated using an 8 × 10 binary pixel grid, in which conductive and etched regions are stochastically generated and subsequently conditioned through connectivity and morphological constraints to ensure physically viable and electrically active antenna structures. Each instance establishes a direct correspondence between a high-resolution representation of the antenna topology and its frequency-domain reflection coefficient (S11) response obtained through full-wave electromagnetic simulations within an automated MATLAB–CST Studio Suite co-simulation workflow. The dataset encompasses different resonance behaviors, including single-band, dual-band, tri-band, and ultra-multiband configurations, with electromagnetic responses evaluated over the 1–8 GHz frequency range. The repository is organized to facilitate both direct research use and progressive learning. It includes the complete raw dataset of paired antenna topologies and electromagnetic responses, as well as a segmented subset specifically prepared for introductory or warm-up exercises, enabling users to become familiar with the data structure, visualization, preprocessing, and analysis procedures before working with the complete collection. In addition, the repository provides Python scripts and supporting resources for data inspection, spectral analysis, visualization, and deep-learning-based antenna topology reconstruction. The structured organization of the data enables researchers and students to independently access antenna geometries and their corresponding electromagnetic responses, reproduce the analyses presented in the associated research, and develop alternative computational approaches. PatchData is intended to serve as a reproducible benchmark for machine learning, artificial intelligence, surrogate modeling, inverse antenna design, electromagnetic response prediction, and automated topology optimization, providing a common data resource for exploring the application of data-driven methods to pixelated microstrip antenna design.