Multifunctional Metasurface Design via Physics‐Simplified Machine Learning Article Swipe
Ruichao Zhu
,
Yajuan Han
,
Yuxiang Jia
,
Sai Sui
,
Tonghao Liu
,
Zuntian Chu
,
Huiting Sun
,
Juanna Jiang
,
Shaobo Qu
,
Jiafu Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1155/int/1492020
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1155/int/1492020
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.
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- article
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- en
- Landing Page
- https://doi.org/10.1155/int/1492020
- OA Status
- hybrid
- Cited By
- 2
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All OpenAlex metadata
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https://openalex.org/W4407633692Canonical identifier for this work in OpenAlex
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https://doi.org/10.1155/int/1492020Digital Object Identifier
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Multifunctional Metasurface Design via Physics‐Simplified Machine LearningWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Ruichao Zhu, Yajuan Han, Yuxiang Jia, Sai Sui, Tonghao Liu, Zuntian Chu, Huiting Sun, Juanna Jiang, Shaobo Qu, Jiafu WangList of authors in order
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https://doi.org/10.1155/int/1492020Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1155/int/1492020Direct OA link when available
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Computer science, Physics, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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| abstract_inverted_index.Recently, | 14 |
| abstract_inverted_index.anomalous | 135 |
| abstract_inverted_index.attracted | 20 |
| abstract_inverted_index.efficient | 16 |
| abstract_inverted_index.flexibly, | 6 |
| abstract_inverted_index.focusing, | 137 |
| abstract_inverted_index.functions | 118, 132 |
| abstract_inverted_index.increased | 54 |
| abstract_inverted_index.intensive | 21 |
| abstract_inverted_index.regulated | 109 |
| abstract_inverted_index.resulting | 51 |
| abstract_inverted_index.satellite | 194 |
| abstract_inverted_index.attentions | 22 |
| abstract_inverted_index.complexity | 55 |
| abstract_inverted_index.decoupling | 85 |
| abstract_inverted_index.functional | 12 |
| abstract_inverted_index.integrated | 141, 182 |
| abstract_inverted_index.manipulate | 2 |
| abstract_inverted_index.parameters | 62, 95, 103, 110 |
| abstract_inverted_index.simplified | 105 |
| abstract_inverted_index.simulation | 157 |
| abstract_inverted_index.structural | 94 |
| abstract_inverted_index.underlying | 48 |
| abstract_inverted_index.Metasurface | 0 |
| abstract_inverted_index.increasing. | 63 |
| abstract_inverted_index.interacting | 102 |
| abstract_inverted_index.metasurface | 145, 152, 174 |
| abstract_inverted_index.potentially | 191 |
| abstract_inverted_index.reflection, | 136 |
| abstract_inverted_index.scattering, | 134 |
| abstract_inverted_index.Importantly, | 160 |
| abstract_inverted_index.Performances | 149 |
| abstract_inverted_index.architecture | 59 |
| abstract_inverted_index.conventional | 29 |
| abstract_inverted_index.demonstrated | 155 |
| abstract_inverted_index.inspiration, | 178 |
| abstract_inverted_index.integration. | 13 |
| abstract_inverted_index.measurement. | 159 |
| abstract_inverted_index.metasurfaces | 25, 31, 74, 187 |
| abstract_inverted_index.multiplexing | 79, 124 |
| abstract_inverted_index.optimization | 71 |
| abstract_inverted_index.relationship | 38 |
| abstract_inverted_index.architecture. | 99 |
| abstract_inverted_index.bidirectional | 168 |
| abstract_inverted_index.independently | 108 |
| abstract_inverted_index.verification, | 130 |
| abstract_inverted_index.communications | 195 |
| abstract_inverted_index.multiparameter | 70 |
| abstract_inverted_index.simplification | 169 |
| abstract_inverted_index.electromagnetic | 3 |
| abstract_inverted_index.multifunctional | 24, 73, 186 |
| abstract_inverted_index.machine‐learning‐assisted | 17, 30 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.77559553 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |