%AMozaffar, M.%ABostanabad, R.%AChen, W.%AEhmann, K.%ACao, J.%ABessa, M.%BJournal Name: Proceedings of the National Academy of Sciences; Journal Volume: 116; Journal Issue: 52 %D2019%I %JJournal Name: Proceedings of the National Academy of Sciences; Journal Volume: 116; Journal Issue: 52 %K %MOSTI ID: 10129616 %PMedium: X %TDeep learning predicts path-dependent plasticity %XPlasticity theory aims at describing the yield loci and work hardening of a material under general deformation states. Most of its complexity arises from the nontrivial dependence of the yield loci on the complete strain history of a material and its microstructure. This motivated 3 ingenious simplifications that underpinned a century of developments in this field: 1) yield criteria describing yield loci location; 2) associative or nonassociative flow rules defining the direction of plastic flow; and 3) effective stress–strain laws consistent with the plastic work equivalence principle. However, 2 key complications arise from these simplifications. First, finding equations that describe these 3 assumptions for materials with complex microstructures is not trivial. Second, yield surface evolution needs to be traced iteratively, i.e., through a return mapping algorithm. Here, we show that these assumptions are not needed in the context of sequence learning when using recurrent neural networks, diverting the above-mentioned complications. This work offers an alternative to currently established plasticity formulations by providing the foundations for finding history- and microstructure-dependent constitutive models through deep learning. %0Journal Article