Physics‐Informed Machine Learning for Optical Modes in Composites

Abstract

Herein, example of study of optical modes propagating through spatially periodic composites is used to demonstrate that embedding physics-driven constraints into machine-learning process can dramatically improve accuracy and generalizability of resulting models. Comprehensive analysis of common compromises between direct physics-based solutions, machine-learning training and deployment times, as well as accuracies of the resulting models is presented. The approach to physics-informed machine learning, presented in this work, can be readily utilized in other situations mapped onto an eigenvalue problem, a known bottleneck of computational electrodynamics. Crucially, the technique provides a way to train the model on configurations with no known solutions. The approach presented in this work can be utilized for machine-learning-driven design, optimization, and characterization of composites with 1D and 2D structure. Physics-informed design of machine learning can be further used to produce high-quality models, in particular, in situations where exact solutions are scarce or are slow to come up with.