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Deep artificial neural networks are powerful tools with many possibleapplications in nanophotonics. Here, we demonstrate how a deep neural networkcan be used as a fast, general purpose predictor of the full near-field andfar-field response of plasmonic and dielectric nanostructures. A trained neuralnetwork is shown to infer the internal fields of arbitrary three-dimensionalnanostructures many orders of magnitude faster compared to conventionalnumerical simulations. Secondary physical quantities are derived from the deeplearning predictions and faithfully reproduce a wide variety of physicaleffects without requiring specific training. We discuss the strengths andlimitations of the neural network approach using a number of model studies ofsingle particles and their near-field interactions. Our approach paves the wayfor fast, yet universal methods for design and analysis of nanophotonicsystems.

Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures