  The purpose of this work is the development of an artificial neural network
(ANN) for surrogate modeling of the mechanical response of viscoplastic grain
microstructures. To this end, a U-Net-based convolutional neural network (CNN)
is trained to account for the history dependence of the material behavior. The
training data take the form of numerical simulation results for the von Mises
stress field under quasi-static tensile loading. The trained CNN (tCNN) can
accurately reproduce both the average response as well as the local von Mises
stress field. The tCNN calculates the von Mises stress field of grain
microstructures not included in the training dataset about 500 times faster
than its calculation based on the numerical solution with a spectral solver of
the corresponding initial-boundary-value problem. The tCNN is also successfully
applied to other types of microstructure morphologies (e.g., matrix-inclusion
type topologies) and loading levels not contained in the training dataset.
