🤖 AI Summary
To address the challenge of efficiently generating large-scale, high-fidelity electromagnetic (EM) simulation data required for machine learning in antenna design, this work develops an open-source, GPU-accelerated EM simulation framework built upon gprMax. By deeply integrating CUDA parallel computing, the framework achieves substantial throughput improvement while maintaining sub-millimeter spatial resolution; its accuracy is experimentally validated to match that of leading commercial EM solvers. The framework further incorporates diverse machine learning and deep learning models to enable high-accuracy inverse prediction—from EM responses to antenna geometry and material parameters. Experimental results demonstrate speedups of over 5× on entry-level GPUs versus high-end CPUs, and up to 18× on flagship gaming GPUs, drastically reducing dataset generation time. This work establishes a reproducible, scalable, and cost-effective simulation–learning infrastructure for data-driven intelligent antenna design.
📝 Abstract
This study proposes an antenna simulation framework powered by graphics processing units (GPUs) based on an open-source electromagnetic (EM) simulation software (gprMax) for machine learning applications of antenna design and optimization. Furthermore, it compares the simulation results with those obtained through commercial EM software. The proposed software framework for machine learning and surrogate model applications will produce antenna data sets consisting of a large number of antenna simulation results using GPUs. Although machine learning methods can attain the optimum solutions for many problems, they are known to be data-hungry and require a great deal of samples for the training stage of the algorithms. However, producing a sufficient number of training samples in EM applications within a limited time is challenging due to the high computational complexity of EM simulations. Therefore, GPUs are utilized in this study to simulate a large number of antennas with predefined or random antenna shape parameters to produce data sets. Moreover, this study also compares various machine learning and deep learning models in terms of antenna parameter estimation performance. This study demonstrates that an entry-level GPU substantially outperforms a high-end CPU in terms of computational performance, while a high-end gaming GPU can achieve around 18 times more computational performance compared to a high-end CPU. Moreover, it is shown that the open-source EM simulation software can deliver similar results to those obtained via commercial software in the simulation of microstrip antennas when the spatial resolution of the simulations is sufficiently fine.