[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123313-en":3,"doc-seo-123313-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123313,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning-Assisted Synthesis of Filtering Antennas Using a Fast Method of Moments Code","This master’s thesis explores how machine learning can be integrated with electromagnetic simulations to optimize antenna designs with faster turnaround and improved performance. An in-house Method of Moments (MoM) software, CAESAR, is used to generate a large dataset efficiently by removing the contribution of Rao-Wilton-Glisson (RWG) basis functions in the MoM matrix. A convolutional neural network (CNN) is trained to predict scattering parameters and gain, while a genetic algorithm refines antenna designs using the trained model. Model accuracy is validated with mean squared error and extensive simulations, showing that CAESAR outperforms CST with the TCST interface by over 2000%, supporting future antenna-design methodology.","Machine Learning-Assisted Synthesis of Filtering Antennas Using a Fast Method of Moments Code  \nMaster’s thesis in Master program Wireless, Photonics and Space  \nFITIM MAXHARRAJ  \nDEPARTMENT OF ELECTRICAL ENGINEERING  \nCHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2024  \n[www.chalmers.se](www.chalmers.se)  \nMaster’s thesis 2024  \nMachine Learning-Assisted Synthesis of Filtering Antennas Using a Fast Method of Moments Code  \nFITIM MAXHARRAJ  \nDepartment of Electrical Engineering Division of Communications, Antennas, and Optical Networks  \nAntenna Systems  \nChalmers University of Technology Gothenburg, Sweden 2024  \nMachine Learning-Assisted Synthesis of Filtering Antennas Using a Fast Method of Moments Code  \nFITIM MAXHARRAJ  \n© FITIM MAXHARRAJ, 2024 .  \nExaminer: Prof. Rob Maaskant, Chalmers  \nSupervisor: Prof. Rob Maaskant, Chalmers  \nCo-Supervisor: Dr. Martin Sjödin, Ericsson  \nMaster’s Thesis 2024  \nDepartment of Electrical Engineering  \nDivision of Communications, Antennas, and Optical Networks Antenna Systems  \nChalmers University of Technology SE-412 96 Gothenburg  \nCover: Visualization of radiation pattern of a ML-model antenna.  \nTypeset in LATEX  \nPrinted by Chalmers Reproservice Gothenburg, Sweden 2024  \nMachine Learning-Assisted Synthesis of Filtering Antennas Using a Fast Method of Moments Code  \nFITIM MAXHARRAJ  \nDepartment of Electrical Engineering Chalmers University of Technology  \nAbstract  \nThis thesis investigates the integration of machine learning algorithms with electromagnetic 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.  \nThe 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.  \nKeywords: Machine Learning, Filtering Antennas, Method of Moments, Electromagnetic Simulation, Optimization.  \nAcknowledgements  \nI would like to extend my sincere thanks to Prof. Rob Maaskant for his invaluable mentorship and guidance throughout my Bachelor’s Thesis and now my Master’s Thesis. Without Rob’s support, this thesis would not have been possible.  \nI am also deeply grateful to my supervisor, Dr. Martin Sjödin, for his exceptional expertise in machine learning.  \nLastly, I would like to express my profound gratitude to my family and my wife, Flaureta Rexhaj, for their unwavering love and support throughout my academic journey.  \nFitim Maxharraj, Gothenburg, June 2024  \nList of Acronyms  \nBelow is the list of acronyms that have been used throughout this thesis listed in alphabetical order:  \nAI ANN BPFCEM CNN EFIEFEMFDTD GA GPU MAPEMFIE ML MoM MSEReLURWGSGDSVDPEC  \nPMCHWT  \nArtificial Intelligence  \nArtificial Neural Network Band-Pass Filter Computational Electromagnetics Convolutional Neural Network Electric Field Integral Equation Finite Element Method  \nFinite Difference Time domain Genetic Algorithm  \nGraphics Processing Unit  \nMean Absolute Percentag","cbCaisAquk6NXZre","https://ap.wps.com/l/cbCaisAquk6NXZre","pdf",2232789,1,70,"English","en",105,"# Abstract\n# Keywords\n# Acknowledgements\n# List of Acronyms","[{\"question\":\"What problem does the thesis address in antenna design?\",\"answer\":\"It targets the challenge of achieving faster and more efficient electromagnetic simulations while optimizing antenna performance for modern antenna systems.\"},{\"question\":\"How is the dataset for training generated?\",\"answer\":\"The thesis uses the in-house MoM software CAESAR to rapidly generate a comprehensive antenna dataset, improving efficiency by removing the contribution of RWG basis functions in the MoM matrix.\"},{\"question\":\"Which models are used for prediction and optimization?\",\"answer\":\"A convolutional neural network (CNN) predicts scattering parameters and gain, and a genetic algorithm optimizes antenna designs using the trained CNN model.\"}]","Machine Learning-Assisted Synthesis of Filtering Antennas Using a Fast Method of Moments Code | 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problem does the thesis address in antenna design?","Question",{"text":75,"@type":76},"It targets the challenge of achieving faster and more efficient electromagnetic simulations while optimizing antenna performance for modern antenna systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset for training generated?",{"text":80,"@type":76},"The thesis uses the in-house MoM software CAESAR to rapidly generate a comprehensive antenna dataset, improving efficiency by removing the contribution of RWG basis functions in the MoM matrix.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models are used for prediction and optimization?",{"text":84,"@type":76},"A convolutional neural network (CNN) predicts scattering parameters and gain, and a genetic algorithm optimizes antenna designs using the trained CNN 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