Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review

By O. Khatib, Simiao Ren, Jordan M. Malof, Willie J Padilla. Advanced Functional Materials, 2021

192 citations recorded by the source corpus.

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.

Subfields: Atomic and Molecular Physics, and Optics, Developmental Biology, Electronic, Optical and Magnetic Materials

View Paper: https://www.semanticscholar.org/paper/b4dfad50a7201e315ac61299038dda0b396dc9a6

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In Atomic and Molecular Physics, and Optics

In Electronic, Optical and Magnetic Materials

Citations in the corpus

The 2023 terahertz science and technology roadmap (2023, background): “deep learning will allow more sophisticated terahertz devices and responses than what has been shown before, while enabling physics informed neural networks to uncover the underlying terahertz science [106, 107].”