[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125795-en":3,"doc-seo-125795-105":30,"detail-sidebar-cat-0-en-105":90},{"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},125795,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Enhancing Performance of Machine Learning-Based Modeling of Electromagnetic Structures","Machine learning (ML)–based modeling of electromagnetic (EM) structures uses surrogate models to approximate the mapping between EM geometries and responses such as S11 and gain. Surrogate accuracy and validity are mainly constrained by the simulation data used for training, which is often collected through uniform parameter sweeps. Limited computational power restricts sampled parameter space, yielding strong behavior inside the sweep range but deteriorating performance when extrapolating. This work improves prediction range at equal simulation cost by optimizing the data acquisition strategy, achieving higher accuracy over an extended parameter space and demonstrating effectiveness via an application example.","Aalborg Universitet  \nEnhancing Performance of Machine Learning-Based Modeling of Electromagnetic Structures  \nZhou, Zhao; Wei, Zhaohui; Ren, Jian; Yin, Yingzeng; Pedersen, Gert Frølund; Shen, Ming  \nPublished in:  \n2023 IEEE Conference on Antenna Measurements and Applications (CAMA)  \nDOI (link to publication from Publisher):  \n10.1109/CAMA57522.2023.10352704  \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. , Yin, Y. , Pedersen, G. F. , & Shen, M. (2023) . Enhancing Performance of Machine Learning-Based Modeling of Electromagnetic Structures. In 2023 IEEE Conference on Antenna Measurements and Applications (CAMA) (pp. 58-60) . IEEE. [https://doi.org/10.1109/CAMA57522.2023.10352704](https://doi.org/10.1109/CAMA57522.2023.10352704)  \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.  \nEnhancing Performance of Machine Learning-Based Modeling of Electromagnetic Structures  \nZhao Zhou  \nDepartment of Electronic Systems Aalborg University Aalborg, Denmark  \n[https://orcid.org/0000-0001-7895-8129](https://orcid.org/0000-0001-7895-8129)  \nZhaohui Wei  \nDepartment of Electronic Systems Aalborg University Aalborg, Denmark  \n[https://orcid.org/0000-0003-4108-7686](https://orcid.org/0000-0003-4108-7686)  \nJian Ren  \nSchool of Electronic Engineering Xidian University Xi’an, China  \n[https://orcid.org/0000-0001-9899-5963](https://orcid.org/0000-0001-9899-5963)  \nYingzeng Yin  \nSchool of Electronic Engineering Xidian University Xi’an, China  \n[https://orcid.org/0000-0002-9103-9925](https://orcid.org/0000-0002-9103-9925)  \nGert Frølund Pedersen  \nDepartment of Electronic Systems Aalborg University Aalborg, Denmark  \n[https://orcid.org/0000-0002-6570-7387](https://orcid.org/0000-0002-6570-7387)  \nMing Shen  \nDepartment of Electronic Systems Aalborg University Aalborg, Denmark  \n[https://orcid.org/0000-0002-9388-3513](https://orcid.org/0000-0002-9388-3513)  \nAbstract—The machine learning (ML)-based modeling of electromagnetic (EM) structures involves the development of a surrogate model that approximates the relationship between EM geometries and responses, such as S11 , gain, etc. The performance of the surrogate model is mainly affected by the simulation data for training. Normally, the training data is collected by uniformly sweeping the geometric parameters. Restricted by the computation power, only a limited parameter space can be sampled. The trained surrogate model behaves well within the sampling range but deteriorates as the parameter range extends. In this paper, we expand the predictable parameter range of an ML model with the same simulation expense by optimizing the data acquisition strategy. This approach leads to the proposed model demonstrating higher accuracy within an extended parameter space than conventional models, while the simulation consumption remains the same. We present an application example to validate its effectiveness. The proposed modified ML-based design method can potentially improve the performance of surrogate models in real-world applications.  \nIndex Terms—elec","cbCaiiKZHxTDfs9W","https://ap.wps.com/l/cbCaiiKZHxTDfs9W","pdf",366228,1,4,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does ML-based modeling of electromagnetic structures face?\",\"answer\":\"Its performance heavily depends on the training simulation data. Uniform sweeps under limited computation cover only a restricted parameter space, so accuracy drops when predicting beyond that range.\"},{\"question\":\"How does the proposed method improve surrogate model performance?\",\"answer\":\"It expands the predictable parameter range without increasing simulation expense by optimizing the data acquisition strategy used to collect training samples.\"},{\"question\":\"What does the paper use to validate the approach?\",\"answer\":\"An application example is presented to validate the effectiveness of the modified ML-based design method in extending accurate prediction beyond conventional models.\"}]","Enhancing Performance of Machine Learning-Based Modeling of Electromagnetic Structures | PDF",1785901243,10,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"enhancing-performance-of-machine-learning-based-modeling-of-electromagnetic-structures","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/enhancing-performance-of-machine-learning-based-modeling-of-electromagnetic-structures/125795/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does ML-based modeling of electromagnetic structures face?","Question",{"text":74,"@type":75},"Its performance heavily depends on the training simulation data. Uniform sweeps under limited computation cover only a restricted parameter space, so accuracy drops when predicting beyond that range.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed method improve surrogate model performance?",{"text":79,"@type":75},"It expands the predictable parameter range without increasing simulation expense by optimizing the data acquisition strategy used to collect training samples.",{"name":81,"@type":72,"acceptedAnswer":82},"What does the paper use to validate the approach?",{"text":83,"@type":75},"An application example is presented to validate the effectiveness of the modified ML-based design method in extending accurate prediction beyond conventional models.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]