A deep neural network for generalized prediction of the near fields and far fields of arbitrary 3D nanostructures

Neural networks are powerful tools with many possible new applications in nanophotonics. Here, we show how a deep neural network is capable to develop a generalized model of light-matter interactions in both plasmonic and dielectric nanostructures. Using the local geometry as an input, the model infers the internal fields inside the nanostructures from which secondary quantities can be derived such as near-field distributions, far-field patterns and optical cross sections. The neural network successfully captures plasmonic effects and antenna resonances in metals, magneto-electric modes, anapole and Kerker effects in high-index dielectrics, as well as near-field interactions including induced chirality. The neural network is up to five orders faster than conventional simulations, paving the way for real time control and optimization schemes.