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• W3207907443 abstract "Conventional metamaterial design mainly relies on manual trial‐and‐error design and optimization to achieve target electromagnetic responses. When faced with high‐degree‐of‐freedom application design, it is impossible to achieve an efficient overall design of massive metamaterial structural units. Herein, a new approach for using ensemble learning for objective‐driven easy‐to‐process metamaterial design. From the perspective of data and learners, reduce the complexity of data preprocessing and achieve accurate closed‐loop design by improving the overall performance of the learning model. The proposed framework overcomes some core problems, which have limited the previous design solutions based on a single model/network: adaptive design of different metamaterial objects, input/output vector‐dimensional mismatch, precise prediction of amplitude value at the resonance frequency, lower data acquisition cost, and difficult to process. In the future, researchers can use the proposed method, integrating cutting‐edge machine learning models and algorithms, to design a variety of metamaterial devices." @default.
• W3207907443 created "2021-10-25" @default.
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• W3207907443 date "2021-10-10" @default.
• W3207907443 modified "2023-10-13" @default.
• W3207907443 title "A Bidirectional Ensemble‐Learning Framework for Target‐Oriented Metamaterial Designs" @default.
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• W3207907443 doi "https://doi.org/10.1002/adpr.202100158" @default.
• W3207907443 hasPublicationYear "2021" @default.
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