Prediction of Antenna Performance based on Scalable Data-informed Machine Learning Methods.

This paper proposes a scalable architecture for predicting antenna performance using various datainformed machine learning (DIML) methods. By utilizing the computation power of graphics processing units (GPUs), the architecture takes advantage of hardware (HW) acceleration from the beginning of electromagnetic (EM) full-wave simulation to the final machine learning (ML) validation. A total of 49152 full-wave simulations of a classical microwave patch antenna forms the ML dataset. The dataset contains the performance of patch antenna on six commonly used materials and two standard thicknesses in a wide frequency range from 0.1 to 20 GHz. A total of 13 base ML models are stacked and ensembled in a tabular workflow with performance as 0.970 and 0.933 F1 scores for two classification models, as well as 0.912 and 0.819 R2 scores for two regression models. Moreover, an image-based workflow is proposed. The image-based workflow yields the 0.823 R2 score, indicating a near real-time prediction for all S11 values from 0.1 to 20 GHz. The proposed architecture requires neither the fine-tuned hyperparameters in the ML-assisted optimization (MLAO) model for specified antenna design nor the pre-knowledge required in the physics-informed models. The fully automated process with data collection and the customized ML pipeline provides the architecture with robust scalability in future work where more antenna types, materials, and performance requirements can be involved. Also, it could be wrapped as a pre-trained ML model as a reference for other antenna designs. [ABSTRACT FROM AUTHOR]