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We present a machine learning approach that allows to characterize thedisorder potential of a two-dimensional electronic system from its quantumtransport properties. Numerically simulated transport data for a large numberof disorder configurations is used for the training of artificial neuralnetworks. We show that the trained networks are able to recognize details ofthe disorder potential of an unknown sample from its transport properties, andthat they can even reconstruct the complete potential landscape seen by theelectrons.

Deep neural networks for inverse problems in mesoscopic physics: Characterization of the disorder configuration from quantum transport properties