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.
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