The best of both worlds: fast and accurate prediction of meta-optics with physics-guided machine learning

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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Published
2024-10-02
DOI
https://doi.org/10.1117/12.3027630
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
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article

The best of both worlds: fast and accurate prediction of meta-optics with physics-guided machine learning

Viktor A. Podolskiy, Daniel Wasserman, Jacob LaMountain, Anuj Karpatne et al.
Computational Physics and Python Applications
article

The best of both worlds: fast and accurate prediction of meta-optics with physics-guided machine learning

Viktor A. Podolskiy, Daniel Wasserman, Jacob LaMountain, Anuj Karpatne, Jie Bu, Bo Fan, Amogh Raju, Sean Lynch
article en

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.

University of Massachusetts Lowell (US), Virginia Tech (US), The University of Texas at Austin (US)
Openalex Percentile: Top 85%
Computational Physics and Python Applications
DOISourceOpenAlex
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The best of both worlds: fast and accurate prediction of meta-optics with physics-guided machine learning — Viktor A. Podolskiy, Daniel Wasserman, et al. · (2024) | TGRS Research Map | TGRS