  The macroscopic response of short fiber reinforced composites is dependent on
an extensive range of microstructural parameters. Thus, micromechanical
modeling of these materials is challenging and in some cases, computationally
expensive. This is particularly important when path-dependent plastic behavior
is needed to be predicted. A solution to this challenge is to enhance
micromechanical solutions with machine learning techniques such as artificial
neural networks. In this work, a recurrent deep neural network model is trained
to predict the path-dependent elasto-plastic stress response of short fiber
reinforced composites, given the microstructural parameters and the strain
path. Micromechanical meanfield simulations are conducted to create a data base
for training the validating the model. The model gives very accurate
predictions in a computationally efficient manner when compared with
independent micromechanical simulations.
