Machine Learning-Assisted Synthesis of Filtering Antennas Using a Fast Method of Moments Code
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Wireless, photonics and space engineering (MPWPS), MSc
Publicerad
2024
Författare
Maxharraj, Fitim
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis investigates the integration of machine learning algorithms with electro magnetic simulations as a novel strategy to optimize antenna designs, thereby significantly enhancing simulation speed and performance in modern antenna systems. The study utilizes an in-house Method of Moments (MoM) software, CAESAR, to rapidly generate a comprehensive dataset of antennas. This is achieved in a timeefficient manner by removing the contribution of Rao-Wilton-Glisson (RWG) basis functions in the MoM matrix. A convolutional neural network (CNN) was selected for its superior pattern recognition capabilities, enabling the model to accurately predict the scattering parameters and gain of various antennas. Furthermore, a genetic algorithm was employed to optimize the antenna designs in conjunction with the trained CNN model. The research includes a thorough validation of the model’s accuracy and reliability, assessed through mean squared error metrics and extensive simulations. Remarkably, the in-house software CAESAR exhibited exceptional efficiency, surpassing commercial software CST coupled with TCST Interface by over 2000%. The results demonstrate the efficiency of combining machine learning with electromagnetic simulations in improving antenna design processes, potentially setting a new standard for future advancements in methodology in antenna design.
Beskrivning
Ämne/nyckelord
Machine Learning , Filtering Antennas , Method of Moments , Electromagnetic Simulation , Optimization