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18 June 2024 Deep Learning the Future of Metamaterials
Willie J. Padilla
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Proceedings Volume PC13017, Machine Learning in Photonics; PC130170B (2024) https://doi.org/10.1117/12.3016504
Event: SPIE Photonics Europe, 2024, Strasbourg, France
Abstract
Deep neural networks (DNNs) are data-driven systems that have transformed traditional research methods and are driving scientific discovery in artificial electromagnetic materials (AEMs). AEMs, including electromagnetic metamaterials, photonic crystals, and plasmonics, are research fields where DNNs have had significant results, validating the data-driven approach, especially for problems where conventional methods have failed. Although the universal approximation theorem indicates that deep learning is a universal solver and therefore can be applied to solve any problem, there are several drawbacks, including the requirement for large training datasets, the unknown size of required datasets, and the black box nature of models (i.e., no access to any physics learned by the model). Through incorporation of prior knowledge, informed deep neural networks can solve many of the outstanding problems in deep learning and may also learn new physics of systems under study. In view of the great potential of deep learning for the future of AEM research, we review the status of the field, focusing on recent advances, open challenges, and future directions.
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Willie J. Padilla "Deep Learning the Future of Metamaterials", Proc. SPIE PC13017, Machine Learning in Photonics, PC130170B (18 June 2024); https://doi.org/10.1117/12.3016504
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KEYWORDS
Deep learning
Electromagnetic metamaterials
Photonic metamaterials
Data modeling
Physics
Systems modeling
Neural networks
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