Machine Learning-Assisted Synthesis of Filtering Antennas Using a Fast Method of Moments Code

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dc.contributor.authorMaxharraj, Fitim
dc.contributor.departmentChalmers tekniska högskola / Institutionen för elektrotekniksv
dc.contributor.examinerMaaskant, Rob
dc.contributor.supervisorMaaskant, Rob
dc.date.accessioned2024-06-13T13:56:43Z
dc.date.available2024-06-13T13:56:43Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractThis 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.
dc.identifier.coursecodeEENX30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/307841
dc.language.isoeng
dc.relation.ispartofseries00000
dc.setspec.uppsokTechnology
dc.subjectMachine Learning
dc.subjectFiltering Antennas
dc.subjectMethod of Moments
dc.subjectElectromagnetic Simulation
dc.subjectOptimization
dc.titleMachine Learning-Assisted Synthesis of Filtering Antennas Using a Fast Method of Moments Code
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeWireless, photonics and space engineering (MPWPS), MSc
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