IDM-Net: A Multi-Task Supported Encoder-Decoder Framework for Magnetic Field Inverse Design
We propose an end-to-end framework for inversely designing permanent magnets named IDM-Net. It utilizes a fundamental encoder-decoder architecture to handle multiple tasks. In more detail, the encoder is responsible for deeply extracting features of the magnetic field and categorizing different type...
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| Vydané v: | IEEE transactions on applied superconductivity Ročník 34; číslo 8; s. 1 - 5 |
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| Hlavní autori: | , |
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| Jazyk: | English |
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New York
IEEE
01.11.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | We propose an end-to-end framework for inversely designing permanent magnets named IDM-Net. It utilizes a fundamental encoder-decoder architecture to handle multiple tasks. In more detail, the encoder is responsible for deeply extracting features of the magnetic field and categorizing different types of magnet shapes. The decoder focuses on inferring significant properties of each specific type of magnet shape, including size, position, and magnetization intensity. Such architecture breaks the critical limitation of designing only a single type of magnet in literature. Further, it allows for flexible choices of encoders' networks, such as convolutional neural networks (CNNs) or transformers, which are widely used in various computer vision tasks. Our experimental results demonstrate that the ResNet50-based and ViT-B/16-based IDM-Nets achieve accuracies of 93.8% and 91.4% in magnet shapes classification and errors of 0.31% and 0.33% in predicting magnetic properties, respectively. |
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| AbstractList | We propose an end-to-end framework for inversely designing permanent magnets named IDM-Net. It utilizes a fundamental encoder-decoder architecture to handle multiple tasks. In more detail, the encoder is responsible for deeply extracting features of the magnetic field and categorizing different types of magnet shapes. The decoder focuses on inferring significant properties of each specific type of magnet shape, including size, position, and magnetization intensity. Such architecture breaks the critical limitation of designing only a single type of magnet in literature. Further, it allows for flexible choices of encoders' networks, such as convolutional neural networks (CNNs) or transformers, which are widely used in various computer vision tasks. Our experimental results demonstrate that the ResNet50-based and ViT-B/16-based IDM-Nets achieve accuracies of 93.8% and 91.4% in magnet shapes classification and errors of 0.31% and 0.33% in predicting magnetic properties, respectively. |
| Author | Wang, Jiaqi Zhang, Qiankun |
| Author_xml | – sequence: 1 givenname: Jiaqi orcidid: 0009-0002-9836-2933 surname: Wang fullname: Wang, Jiaqi email: jiaqiqaq@hust.edu.cn organization: School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China – sequence: 2 givenname: Qiankun orcidid: 0000-0002-8034-2689 surname: Zhang fullname: Zhang, Qiankun email: qiankun@hust.edu.cn organization: School of Cyber Science and Engineering, Huazhong University of Science and Technology, Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, Wuhan, China |
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| SubjectTerms | Artificial neural networks Computer vision Decoding Deep learning design optimization encoder-decoder Encoders-Decoders Inverse design Magnetic confinement magnetic field Magnetic fields Magnetic properties Magnetic resonance imaging Magnetosphere Permanent magnets Shape Superconducting magnets |
| Title | IDM-Net: A Multi-Task Supported Encoder-Decoder Framework for Magnetic Field Inverse Design |
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