  In this work we employ data-driven homogenization approaches to predict the
particular mechanical evolution of polycrystalline aggregates with tens of
individual crystals. In these oligocrystals the differences in stress response
due to microstructural variation is pronounced. Shell-like structures produced
by metal-based additive manufacturing and the like make the prediction of the
behavior of oligocrystals technologically relevant. The predictions of
traditional homogenization theories based on grain volumes are not sensitive to
variations in local grain neighborhoods. Direct simulation of the local
response with crystal plasticity finite element methods is more detailed, but
the computations are expensive. To represent the stress-strain response of a
polycrystalline sample given its initial grain texture and morphology we have
designed a novel neural network that incorporates a convolution component to
observe and reduce the information in the crystal texture field and a recursive
component to represent the causal nature of the history information. This model
exhibits accuracy on par with crystal plasticity simulations at minimal
computational cost per prediction.
