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3 October 2024 The best of both worlds: fast and accurate prediction of meta-optics with physics-guided machine learning
Viktor A. Podolskiy, Sean Lynch, Jacob LaMountain, Jie Bu, Bo Fan, Amogh Raju, Anuj Karpatne, Daniel Wasserman
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Proceedings Volume PC13109, Metamaterials, Metadevices, and Metasystems 2024; PC1310908 (2024) https://doi.org/10.1117/12.3027630
Event: Nanoscience + Engineering, 2024, San Diego, California, United States
Abstract
We aim to address one of the fundamental limitations of machine learning (ML): its reliance on extensive training datasets by incorporating physics-based intuition and Maxwell-equation-based constraints into ML process. We show that physics-guided networks require significantly smaller datasets, enable learning outside the original training data, and provide improved prediction accuracy and physics consistency. The proposed approaches are illustrated on examples of photonic composites, from photonic crystals to hyperbolic metamaterials.
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Viktor A. Podolskiy, Sean Lynch, Jacob LaMountain, Jie Bu, Bo Fan, Amogh Raju, Anuj Karpatne, and Daniel Wasserman "The best of both worlds: fast and accurate prediction of meta-optics with physics-guided machine learning", Proc. SPIE PC13109, Metamaterials, Metadevices, and Metasystems 2024, PC1310908 (3 October 2024); https://doi.org/10.1117/12.3027630
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KEYWORDS
Machine learning
Education and training
Composites
Data modeling
Electromagnetism
Photonic crystals
Hyperbolic metamaterials
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