Deep transfer learning for metamaterials research

Willie J. Padilla

doi:10.1117/12.2664582

13 March 2023 Deep transfer learning for metamaterials research

Willie J. Padilla

Proceedings Volume PC12420, Terahertz, RF, Millimeter, and Submillimeter-Wave Technology and Applications XVI; PC1242005 (2023) https://doi.org/10.1117/12.2664582
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

Deep learning has shown notable results in electromagnetic metamaterials research. However, one of the major outstanding challenges is the required large dataset needed for the training stage of the neural network, which may take several months to complete. In order to mitigate this data bottleneck issue, we demonstrate a transfer learning approach to deep learning, which takes advantage of the similar underlying physics between related problems. We demonstrate transfer learning on metasurfaces with a reduced dataset and produce similar accurate results. We overview current efforts and give an outlook for the future of deep learning metamaterials.

Conference Presentation

Citation Download Citation

Willie J. Padilla "Deep transfer learning for metamaterials research", Proc. SPIE PC12420, Terahertz, RF, Millimeter, and Submillimeter-Wave Technology and Applications XVI, PC1242005 (13 March 2023); https://doi.org/10.1117/12.2664582

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