Data-driven acceleration of photonic simulations

Designing modern photonic devices often involves traversing a large parameter space via an optimization procedure, gradient based or otherwise, and typically results in the designer performing electromagnetic simulations of a large number of correlated devices. In this paper, we investigate the possibility of accelerating electromagnetic simulations using the data collected from such correlated simulations. In particular, we present an approach to accelerate the Generalized Minimal Residual (GMRES) algorithm for the solution of frequency-domain Maxwell's equations using two machine learning models (principal component analysis and a convolutional neural network). These data-driven models are trained to predict a subspace within which the solution of the frequency-domain Maxwell's equations approximately lies. This subspace is then used for augmenting the Krylov subspace generated during the GMRES iterations, thus effectively reducing the size of the Krylov subspace and hence the number of iterations needed for solving Maxwell's equations. By training the proposed models on a dataset of wavelength-splitting gratings, we show an order of magnitude reduction (~10-50) in the number of GMRES iterations required for solving frequency-domain Maxwell's equations.