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AbstractMachine learning offers the potential to revolutionize the inverse design of complex nanophotonic components. Here, we propose a novel variant of this formalism specifically suited for the design of resonant nanophotonic components. Typically, the first step of an inverse design process based on machine learning is training a neural network to approximate the non-linear mapping from a set of input parameters to a given optical system's features. The second step starts from the desireddoi:10.1515/nanoph-2020-0379 fatcat:wk2ai4btnbcafd42xz3hww6i6i