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Industrial applications of conductive polymer composites with carbon nanotubes require precise tailoring of their electrical properties. While existing theoretical methods to predict the bulk conductivity require fitting to experiments and often employ power-laws valid only in the vicinity of the percolation threshold, the accuracy of numerical methods is accompanied with substantial computational efforts. In this paper we use recently developed physically-based finite element analyses to successfully train an artificial neural network to make predictions of the bulk conductivity of CNT-polymer composites at negligible computational cost. Main Conductive composites of carbon nanotubes (CNT) and polymers enable a wide range of applications [1, 2] which require precise tailoring of their electrical properties. Existing theoretical methods [3, 4] to predict the bulk conductivity of such composites require fitting to experiments and often employ power-law approximations which are accurate only close to the

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