Science-informed machine-learning for optical composites (Conference Presentation)

Viktor A. Podolskiy; Abantika Ghosh; Mohannad Elhamod; Jie Bu; Wei-Cheng Lee; Anuj Karpatne

doi:10.1117/12.2632454

3 October 2022 Science-informed machine-learning for optical composites (Conference Presentation)

Viktor A. Podolskiy, Abantika Ghosh, Mohannad Elhamod, Jie Bu, Wei-Cheng Lee, Anuj Karpatne

Proceedings Volume PC12195, Metamaterials, Metadevices, and Metasystems 2022; PC121950Y (2022) https://doi.org/10.1117/12.2632454
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

Machine learning is widely used for optimization or classification tasks. Unfortunately, extensive labeled datasets are often required for training machine learning models. In this work we demonstrate that incorporating physics-driven constraints into machine learning algorithms can dramatically improve both accuracy and extendibility of resulting models, simultaneously reducing the size of the required training set and enabling training on unlabeled data. Physics-informed machine learning is illustrated on example of predicting optical modes supported by periodic layered composites. The approach can be readily utilized for analysis of electromagnetic modes in composites with 2D periodic geometry or in complex waveguiding structures.

Conference Presentation

Citation Download Citation

Viktor A. Podolskiy, Abantika Ghosh, Mohannad Elhamod, Jie Bu, Wei-Cheng Lee, and Anuj Karpatne "Science-informed machine-learning for optical composites (Conference Presentation)", Proc. SPIE PC12195, Metamaterials, Metadevices, and Metasystems 2022, PC121950Y (3 October 2022); https://doi.org/10.1117/12.2632454

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