  Design and analysis of inelastic materials requires prediction of physical
responses that evolve under loading. Numerical simulation of such behavior
using finite element (FE) approaches can call for significant time and
computational effort. To address this challenge, this paper demonstrates a deep
learning (DL) framework that is capable of predicting micro-scale
elasto-plastic strains and stresses in a two-phase medium, at a much greater
speed than traditional FE simulations. The proposed framework uses a deep
convolutional neural network (CNN), specifically a U-Net architecture with 3D
operations, to map the composite microstructure to the corresponding stress and
strain fields under a predetermined load path. In particular, the model is
applied to a two-phase fiber reinforced plastic (FRP) composite microstructure
subjected to a given loading-unloading path, predicting the corresponding
stress and strain fields at discrete intermediate load steps. A novel two-step
training approach provides more accurate predictions of stress, by first
training the model to predict strain fields and then using those strain fields
as input to the model that predicts the stress fields. This efficient
data-driven approach enables accurate prediction of physical fields in
inelastic materials, based solely on microstructure images and loading
information.
