  Crystal plasticity theory is often employed to predict the mesoscopic states
of polycrystalline metals, and is well-known to be costly to simulate. Using a
neural network with convolutional layers encoding correlations in time and
space, we were able to predict the evolution of the stress field given only the
initial microstructure and external loading. In comparison to our recent work
we were able to predict not only the spatial average of the stress response but
the field itself. We show that the stress fields and their rates are in high
fidelity with the crystal plasticity data and have no visible artifacts.
Furthermore the distribution stress throughout the elastic to fully plastic
transition match the truth provided by held out crystal plasticity data. Lastly
we demonstrate the efficacy of the trained model in material characterization
and optimization tasks.
