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.
We demonstrate that incorporating physics-based intuition and Maxwell-equation-based constraints into machine learning process reduces the required amount of the training data and improves prediction accuracy and physics consistency. In addition, physics-based provides an avenue to extend the range of the model applicability outside the space of the original labeled dataset. The proposed approaches are illustrated on examples of photonic composites, from photonic crystals to hyperbolic metamaterials.
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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