Learning faster and better: embedding Maxwell equations into machine learning

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.