  We propose a method to use artificial neural networks to approximate light
scattering by multilayer nanoparticles. We find the network needs to be trained
on only a small sampling of the data in order to approximate the simulation to
high precision. Once the neural network is trained, it can simulate such
optical processes orders of magnitude faster than conventional simulations.
Furthermore, the trained neural network can be used solve nanophotonic inverse
design problems by using back- propogation - where the gradient is analytical,
not numerical.
