Teaching optics to a machine learning network

In this Letter, we demonstrate how harmonic oscillator equations can be integrated in a neural network to improve the spectral response prediction for an optical system. We use the optical properties of a one-dimensional nanoslit array for a practical implementation of the study. This method allows to build more generalizable relations between the input parameters of the array and its optical properties, showing a 20-fold improvement for parameters outside the range used for the training. We also show how this model generates the output spectrum from phenomenological relationships between the input parameters and the output spectrum, indicating how it grasps the physical mechanisms of the optical response of the structure.