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Multifunctional Metasurface Design via Physics‐Simplified Machine Learning.

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Title: Multifunctional Metasurface Design via Physics‐Simplified Machine Learning.
Authors: Zhu, Ruichao1 (AUTHOR), Han, Yajuan1,2 (AUTHOR) mshyj_mail@126.com, Jia, Yuxiang1,2 (AUTHOR), Sui, Sai1 (AUTHOR), Liu, Tonghao1 (AUTHOR), Chu, Zuntian1 (AUTHOR), Sun, Huiting1 (AUTHOR), Jiang, Juanna1 (AUTHOR), Qu, Shaobo1 (AUTHOR), Wang, Jiafu1,2 (AUTHOR) wangjiafu1981@126.com, Rajamohan, Vasudevan (AUTHOR)
Source: International Journal of Intelligent Systems. 2/17/2025, Vol. 2025, p1-12. 12p.
Subjects: Functional integration, Telecommunication satellites, Machine learning, Holography, Multiplexing
Abstract: Metasurface can manipulate electromagnetic (EM) waves flexibly, which provides the basis for functional integration. Recently, the efficient machine‐learning‐assisted methods have attracted intensive attentions in multifunctional metasurfaces design. However, the conventional machine‐learning‐assisted metasurfaces design is to fit the internal relationship in the form of black box, which ignores the underlying physical logic, resulting in the increased complexity of machine learning architecture with the parameters increasing. In order to adapt to the multiparameter optimization in multifunctional metasurfaces design, we propose a multiplexing neural network (MNN) based on decoupling at the physical layer to simplify both the structural parameters and the network architecture. The four interacting parameters are simplified into four independently regulated parameters so that the facile design of four functions can be realized only by multiplexing a simple neural network. For verification, four functions of scattering, anomalous reflection, focusing, and hologram are integrated in the same metasurface aperture by MNN. Performances of the metasurface are fully demonstrated by simulation and measurement. Importantly, this work paves the way for the bidirectional simplification of machine learning and metasurface design via physical inspiration, which provides an integrated design method of multifunctional metasurfaces and can be potentially applied to satellite communications and other fields. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Multifunctional Metasurface Design via Physics‐Simplified Machine Learning.
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  Data: <searchLink fieldCode="AR" term="%22Zhu%2C+Ruichao%22">Zhu, Ruichao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Yajuan%22">Han, Yajuan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> mshyj_mail@126.com</i><br /><searchLink fieldCode="AR" term="%22Jia%2C+Yuxiang%22">Jia, Yuxiang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sui%2C+Sai%22">Sui, Sai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Tonghao%22">Liu, Tonghao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chu%2C+Zuntian%22">Chu, Zuntian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Huiting%22">Sun, Huiting</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Juanna%22">Jiang, Juanna</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qu%2C+Shaobo%22">Qu, Shaobo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Jiafu%22">Wang, Jiafu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> wangjiafu1981@126.com</i><br /><searchLink fieldCode="AR" term="%22Rajamohan%2C+Vasudevan%22">Rajamohan, Vasudevan</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Intelligent+Systems%22">International Journal of Intelligent Systems</searchLink>. 2/17/2025, Vol. 2025, p1-12. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Functional+integration%22">Functional integration</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunication+satellites%22">Telecommunication satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Holography%22">Holography</searchLink><br /><searchLink fieldCode="DE" term="%22Multiplexing%22">Multiplexing</searchLink>
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  Label: Abstract
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  Data: Metasurface can manipulate electromagnetic (EM) waves flexibly, which provides the basis for functional integration. Recently, the efficient machine‐learning‐assisted methods have attracted intensive attentions in multifunctional metasurfaces design. However, the conventional machine‐learning‐assisted metasurfaces design is to fit the internal relationship in the form of black box, which ignores the underlying physical logic, resulting in the increased complexity of machine learning architecture with the parameters increasing. In order to adapt to the multiparameter optimization in multifunctional metasurfaces design, we propose a multiplexing neural network (MNN) based on decoupling at the physical layer to simplify both the structural parameters and the network architecture. The four interacting parameters are simplified into four independently regulated parameters so that the facile design of four functions can be realized only by multiplexing a simple neural network. For verification, four functions of scattering, anomalous reflection, focusing, and hologram are integrated in the same metasurface aperture by MNN. Performances of the metasurface are fully demonstrated by simulation and measurement. Importantly, this work paves the way for the bidirectional simplification of machine learning and metasurface design via physical inspiration, which provides an integrated design method of multifunctional metasurfaces and can be potentially applied to satellite communications and other fields. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/int/1492020
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      – Code: eng
        Text: English
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        PageCount: 12
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    Subjects:
      – SubjectFull: Functional integration
        Type: general
      – SubjectFull: Telecommunication satellites
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Holography
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      – SubjectFull: Multiplexing
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      – TitleFull: Multifunctional Metasurface Design via Physics‐Simplified Machine Learning.
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              M: 02
              Text: 2/17/2025
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              Y: 2025
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