[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123992-en":3,"doc-seo-123992-105":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},123992,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine-learning surrogate modeling approaches used in electromechanical systems and electrical components - Bachelor’s thesis","This bachelor’s thesis investigates machine-learning surrogate modeling approaches for electromechanical systems and electrical components. Electromechanical systems integrate mechanical, electronic, and computational technologies, creating complex behavior that requires accurate coordination and reliable data processing, particularly under unknown or varying operating conditions. Surrogate models built from finite element data can simulate and predict system behavior in advance to support design, control development, and performance optimization. The work studies neural-network-based surrogates, including data collection, preprocessing, model training, and evaluation. It also analyzes advantages and limitations, reduces reliance on physical experiments and complex analytical modeling, and discusses open challenges and future development directions for more intelligent automation.","MACHINE-LEARNING SURROGATE MODELING APPROACHES USED IN ELECTROMECHANICAL SYSTEMS AND ELECTRICAL COMPONENTS  \nLappeenranta–Lahti University of Technology LUT  \nBachelor’s programme in Electrical Engineering, Bachelor's thesis  \n2024  \nYaxuan Liu  \nExaminer(s): Assistant Professor Niko Nevaranta  \nABSTRACT  \nLappeenranta–Lahti University of Technology LUTLUT School of Energy Systems  \nElectrical Engineering  \nYaxuan Liu  \nMachine-learning surrogate modeling approaches used in electromechanical systems and electrical components  \nBachelor’s thesis 2024  \n27 pages, 3 figures, 1 table  \nExaminer(s): Assistant Professor Niko Nevaranta  \nKeywords: Deep Learning, Electromechanical Systems, Finite Element Models, Machine Learning, Neural Networks, Surrogate Modeling.  \nThis bachelor’s thesis focuses on machine learning surrogate modeling approach used in electromechanical systems and electrical components. Modern electromechanical systems essentially include mechanical, electronic, and computational technologies which could create complex tasks systems. These complexity systems need precise coordination in components and data processing, especially in unknown conditions. As a result, surrogate modeling is particularly important in these conditions. These model systems with data-based can simulate and predict the behavior of electromechanical systems in advance, which is important in system design, control strategy formulation and performance optimization. This thesis will focus on surrogate modeling methods of finite element models with neural networks. The thesis will also explore how to use data learning to optimize surrogate models, which included all aspects of data collection, processing, and model training. In addition, it also discusses the advantages and limitations of machine learning surrogate modeling approach and will present its application in electromechanical systems.  \nFinally, the thesis discusses the challenges of current methods and possible directions for future development. Surrogate models reduce the system's dependence on physical experiments and complex mathematical modeling, which is an important method for design and analysis of electromechanical systems and electrical components. It will make a important position in intelligence and automation in the future.  \nACKNOWLEDGEMENTS  \nDuring my bachelor's degree studies at LUT, it’s my pleasure to study with my supervisor, Professor Niko Nevaranta. Professor Niko corrects my thesis carefully every time and gives me guidance and help patiently. He also encouraged me to think independently. I am very happy to complete my thesis with the guidance and help of Professor Niko. Here, I would like to express my deep respect and gratitude to Professor Niko.  \nFinally, I would also like to express my gratitude to my family and friends. Their encouragement was an important motivation for me to complete my bachelor's degree. Especially my father’s teachings to me. I would like to thank my father for sending me to study at LUT in Finland.  \nSYMBOLS AND ABBREVIATIONS  \nRoman characters  \nx the feature variable to be normalized  \nxscaled the resulting normalized value  \nxmin the minimum value  \nxmax the maximum value  \nµ the mean value of the feature variable  \nσ the standard deviation of the feature variable  \nθ the model parameters  \nu the time series input  \nAbbreviations  \nAI  \nANNs  \nCNN  \nEM  \nFEM  \nGBDT  \nGP-NARX  \nLSTM  \nML  \nNN  \nPMSM  \nArtificial intelligence  \nArtificial neural networks  \nConvolutional neural network  \nElectrical machine  \nFinite element method  \nGradient boosting decision trees  \nGaussian process regression  \nLong short-term memory network Machine learning  \nNeural network  \nPermanent magnet synchronous machine  \nTable of contents  \nAbstract  \n(Acknowledgements)  \n(Symbols and abbreviations)  \n1 Introduction .................................................................................................................... 7  \n2 Machine-learning surro","cbCaiuUqAaKKug9C","https://ap.wps.com/l/cbCaiuUqAaKKug9C","pdf",426116,1,27,"English","en",105,"# Abstract\n# Acknowledgements\n# Symbols and abbreviations\n# 1 Introduction\n# 2 Machine-learning surrogate modelling approaches\n## 2.1 System modeling and data collection\n## 2.2 Data processing\n## 2.3 Model training\n## 2.4 Model selection and decision\n# 3 Examples of machine-learning surrogate modelling in electromechanical systems\n## 3.1 Case example 1: surrogate modeling of electrical machine torque using artificial neural networks\n## 3.2 Case example 2: surrogate modeling of nonlinear dynamic systems\n## 3.3 Disadvantages and advantages in surrogate modeling\n# 4 Conclusions\n# References","[{\"question\":\"What is the core goal of this thesis?\",\"answer\":\"To study machine-learning surrogate modeling methods for electromechanical systems and electrical components, with emphasis on finite element models and neural networks.\"},{\"question\":\"Why are surrogate models important for these systems?\",\"answer\":\"They help simulate and predict behavior in advance, reducing dependence on physical experiments and complex mathematical modeling, especially in unknown operating conditions.\"},{\"question\":\"What tasks does the thesis cover for building surrogate models?\",\"answer\":\"It examines data collection, processing steps such as normalization and handling missing or imbalanced data, and model training and selection for surrogate performance.\"}]","Machine-learning surrogate modeling approaches used in electromechanical systems and electrical components - Bachelor’s thesis | PDF",1785819700,68,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-surrogate-modeling-approaches-used-in-electromechanical-systems-and-electrical-components-bachelors-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@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":53},"https://docshare.wps.com/document/machine-learning-surrogate-modeling-approaches-used-in-electromechanical-systems-and-electrical-components-bachelors-thesis/123992/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the core goal of this thesis?","Question",{"text":75,"@type":76},"To study machine-learning surrogate modeling methods for electromechanical systems and electrical components, with emphasis on finite element models and neural networks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are surrogate models important for these systems?",{"text":80,"@type":76},"They help simulate and predict behavior in advance, reducing dependence on physical experiments and complex mathematical modeling, especially in unknown operating conditions.",{"name":82,"@type":73,"acceptedAnswer":83},"What tasks does the thesis cover for building surrogate models?",{"text":84,"@type":76},"It examines data collection, processing steps such as normalization and handling missing or imbalanced data, and model training and selection for surrogate performance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]