A Review of Data-Driven Models for Electromagnetic Devices Design and Analysis
In recent years, the design and optimization of electromagnetic devices have grown increasingly complex, driven by the demand for higher efficiency, greater power density, and cost-effectiveness. Traditional approaches such as finite element analysis (FEA) offer precise simulations but can be time-c...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11091428/ |
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| author | Zihan Li Mengyu Cheng Andy Tyrrell Xing Zhao |
| author_facet | Zihan Li Mengyu Cheng Andy Tyrrell Xing Zhao |
| author_sort | Zihan Li |
| collection | DOAJ |
| description | In recent years, the design and optimization of electromagnetic devices have grown increasingly complex, driven by the demand for higher efficiency, greater power density, and cost-effectiveness. Traditional approaches such as finite element analysis (FEA) offer precise simulations but can be time-consuming and computationally intensive. To address these challenges, data-driven methods have gained traction as efficient alternatives. This review, focusing on recent deep learning advances, presents a comprehensive review on the application of data-driven models in the design and optimization of electromagnetic devices, summarizing the statistical models such as Response Surface Methodology (RSM) and recent popular machine learning (ML) methods in handling multiple variables, as well as the deep learning (DL) models, in predicting various electromagnetic device parameters and optimizing electromagnetic models. This paper highlights the latest advances in DL models for electromagnetic device applications, including motors, transformers, and electrical wires. It discusses their potential to assist FEA to accelerate design and optimization. Future key directions are proposed to improve efficiency and expand the versatility of data-driven models. |
| format | Article |
| id | doaj-art-d42841fce3084e8bbfac6bc7cb777622 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d42841fce3084e8bbfac6bc7cb7776222025-08-20T03:43:52ZengIEEEIEEE Access2169-35362025-01-011314216114218510.1109/ACCESS.2025.359196511091428A Review of Data-Driven Models for Electromagnetic Devices Design and AnalysisZihan Li0https://orcid.org/0009-0006-2289-5353Mengyu Cheng1https://orcid.org/0000-0002-6483-4480Andy Tyrrell2https://orcid.org/0000-0002-8533-2404Xing Zhao3https://orcid.org/0000-0003-4000-0446School of Physics, Engineering and Technology, University of York, York, U.K.School of Physics, Engineering and Technology, University of York, York, U.K.School of Physics, Engineering and Technology, University of York, York, U.K.School of Physics, Engineering and Technology, University of York, York, U.K.In recent years, the design and optimization of electromagnetic devices have grown increasingly complex, driven by the demand for higher efficiency, greater power density, and cost-effectiveness. Traditional approaches such as finite element analysis (FEA) offer precise simulations but can be time-consuming and computationally intensive. To address these challenges, data-driven methods have gained traction as efficient alternatives. This review, focusing on recent deep learning advances, presents a comprehensive review on the application of data-driven models in the design and optimization of electromagnetic devices, summarizing the statistical models such as Response Surface Methodology (RSM) and recent popular machine learning (ML) methods in handling multiple variables, as well as the deep learning (DL) models, in predicting various electromagnetic device parameters and optimizing electromagnetic models. This paper highlights the latest advances in DL models for electromagnetic device applications, including motors, transformers, and electrical wires. It discusses their potential to assist FEA to accelerate design and optimization. Future key directions are proposed to improve efficiency and expand the versatility of data-driven models.https://ieeexplore.ieee.org/document/11091428/Data-driven modelsdeep learningelectromagnetic devicemachine learningoptimizationsurrogate model |
| spellingShingle | Zihan Li Mengyu Cheng Andy Tyrrell Xing Zhao A Review of Data-Driven Models for Electromagnetic Devices Design and Analysis IEEE Access Data-driven models deep learning electromagnetic device machine learning optimization surrogate model |
| title | A Review of Data-Driven Models for Electromagnetic Devices Design and Analysis |
| title_full | A Review of Data-Driven Models for Electromagnetic Devices Design and Analysis |
| title_fullStr | A Review of Data-Driven Models for Electromagnetic Devices Design and Analysis |
| title_full_unstemmed | A Review of Data-Driven Models for Electromagnetic Devices Design and Analysis |
| title_short | A Review of Data-Driven Models for Electromagnetic Devices Design and Analysis |
| title_sort | review of data driven models for electromagnetic devices design and analysis |
| topic | Data-driven models deep learning electromagnetic device machine learning optimization surrogate model |
| url | https://ieeexplore.ieee.org/document/11091428/ |
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