[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116998-en":3,"doc-seo-116998-105":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},116998,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A Study on Machine Learning Assisted Accelerated Design of Microwave Structures","An efficient framework for machine learning-assisted accelerated design of microwave structures addresses a key bottleneck: training-data preparation demands extensive EM simulations. The study compares machine learning-based design with conventional optimization algorithms by qualitatively analyzing required simulation cycles across the full metasurface design workflow. Results show machine learning offers higher efficiency for high-bit metasurface designs, while optimization algorithms are more efficient for low-bit cases. An improved hybrid approach combines both advantages to enhance overall design efficiency. ","Aalborg Universitet  \nA Study on Machine Learning Assisted Accelerated Design of Microwave Structures  \nZhou, Zhao; Wei, Zhaohui; Ren, Jian; Sun, Nan; Kang, Jiali; Yin, Yingzeng; Shen, Ming  \nPublished in:  \n2023 Photonics and Electromagnetics Research Symposium, PIERS 2023-Proceedings  \nDOI (link to publication from Publisher):  \n10.1109/PIERS59004.2023.10221453  \nPublication date:  \n2023  \nDocument Version  \nAccepted author manuscript, peer reviewed version  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nZhou, Z. , Wei, Z. , Ren, J. , Sun, N. , Kang, J. , Yin, Y. , & Shen, M. (2023) . A Study on Machine Learning Assisted Accelerated Design of Microwave Structures. In 2023 Photonics and Electromagnetics Research Symposium, PIERS 2023-Proceedings (pp. 1189-1192) . Article 10221453 IEEE.  \n[https://doi.org/10.1109/PIERS59004.2023.10221453](https://doi.org/10.1109/PIERS59004.2023.10221453)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n-Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n-You may not further distribute the material or use it for any profit-making activity or commercial gain  \n-You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from [vbn.aau.dk](vbn.aau.dk) on: May 29, 2024  \nA Study on Machine Learning Assisted Accelerated Design of  \nMicrowave Structures  \nZhao Zhou 1 , Zhaohui Wei 1 , Jian Ren2 , Nan Sun3 , Jiali Kang4 , Yingzeng Yin2 , and Ming Shen 1  \n1 Aalborg University, Denmark  \n2Xidian University, China  \n3 Nanjing University of Aeronautics and Astronautics, China  \n4Xi’an Jiaotong University, China  \nAbstract—An increasing number of researchers devote to applying machine learning for accelerating design of microwave structures (e.g., antenna, metasurface, filter, etc.), inspired by the great potential that machine learning shows in many fields, such as image/speech/digits recognition, self-driving, text processing, etc. Despite the fact that machine learning based design has been widely validated to be accurate and well-behaved, machine learning based design methods are often doubted in terms of efficiency, because a large amount of simulation works are mandatory to be executed previously for preparing sufficient training data. In that sense, machine learning based design seems not to be efficient, as it takes more simulation works in total than conventional optimization algorithm based design methods. This paper investigates the efficiency of machine learning based design compared with typical optimization algorithm based design, and a generic solution is proposed for reducing the burden of data preparation to improve the efficiency of machine learning based design. By qualitatively analyzing the required simulation cycles during the whole design process, we propose efficiency measures to demonstrate and compare the efficiency of machine learning based design and typical optimization algorithm based design in the context of metasurface design. According to the comparison result, machine learning based design outperforms other methods in terms of efficiency when it comes to high-bit metasurface design, while optimization algorithm based design is more efficient for low-bit metasurface. Based on the observation, we introduced an improved design approach that combines the advantages of optimization algorithms and machine learning. The qualitative analysis and improved des","cbCaiqoB7lp46WCd","https://ap.wps.com/l/cbCaiqoB7lp46WCd","pdf",644190,1,5,"English","en",105,"# Abstract\n# Introduction\n## Design challenge in microwave structures\n## Optimization algorithm-based design\n## Machine learning-based automated design\n## Efficiency comparison for metasurface design","[{\"question\":\"Why can machine learning-based design be considered inefficient for microwave structures?\",\"answer\":\"It often requires many EM simulation cycles to generate sufficient training data for surrogate models, increasing total simulation effort compared with conventional optimization methods.\"},{\"question\":\"How does the paper evaluate efficiency between machine learning and optimization algorithms?\",\"answer\":\"It qualitatively analyzes the number of simulation cycles needed throughout the design process and uses this to compare approaches in the context of metasurface design.\"},{\"question\":\"Under what metasurface conditions does machine learning outperform optimization algorithms?\",\"answer\":\"Machine learning-based design outperforms other methods in efficiency for high-bit metasurface design, while optimization algorithm-based design is more efficient for low-bit metasurface.\"}]","A Study on Machine Learning Assisted Accelerated Design of Microwave Structures | 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can machine learning-based design be considered inefficient for microwave structures?","Question",{"text":75,"@type":76},"It often requires many EM simulation cycles to generate sufficient training data for surrogate models, increasing total simulation effort compared with conventional optimization methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper evaluate efficiency between machine learning and optimization algorithms?",{"text":80,"@type":76},"It qualitatively analyzes the number of simulation cycles needed throughout the design process and uses this to compare approaches in the context of metasurface design.",{"name":82,"@type":73,"acceptedAnswer":83},"Under what metasurface conditions does machine learning outperform optimization algorithms?",{"text":84,"@type":76},"Machine learning-based design outperforms other methods in efficiency for high-bit metasurface design, while optimization algorithm-based design is more efficient for low-bit 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