  Deep artificial neural networks are powerful tools with many possible
applications in nanophotonics. Here, we demonstrate how a deep neural network
can be used as a fast, general purpose predictor of the full near-field and
far-field response of plasmonic and dielectric nanostructures. A trained neural
network is shown to infer the internal fields of arbitrary three-dimensional
nanostructures many orders of magnitude faster compared to conventional
numerical simulations. Secondary physical quantities are derived from the deep
learning predictions and faithfully reproduce a wide variety of physical
effects without requiring specific training. We discuss the strengths and
limitations of the neural network approach using a number of model studies of
single particles and their near-field interactions. Our approach paves the way
for fast, yet universal methods for design and analysis of nanophotonic
systems.
