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- W3168693657 abstract "Abstract Deep neural networks (DNNs) are empirically derived systems that have transformed traditional research methods, and are driving scientific discovery. Artificial electromagnetic materials (AEMs)—including electromagnetic metamaterials, photonic crystals, and plasmonics—are research fields where DNN results valorize the data driven approach; especially in cases where conventional methods have failed. In view of the great potential of deep learning for the future of artificial electromagnetic materials research, the status of the field with a focus on recent advances, key limitations, and future directions is reviewed. Strategies, guidance, evaluation, and limits of using deep networks for both forward and inverse AEM problems are presented." @default.
- W3168693657 created "2021-06-22" @default.
- W3168693657 creator A5007239332 @default.
- W3168693657 creator A5022100994 @default.
- W3168693657 creator A5048895233 @default.
- W3168693657 creator A5077837082 @default.
- W3168693657 date "2021-05-28" @default.
- W3168693657 modified "2023-10-14" @default.
- W3168693657 title "Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review" @default.
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