  An image-based deep learning framework is developed in this paper to predict
damage and failure in microstructure-dependent composite materials. The work is
motivated by the complexity and computational cost of high-fidelity simulations
of such materials. The proposed deep learning framework predicts the
post-failure full-field stress distribution and crack pattern in
two-dimensional representations of the composites based on the geometry of
microstructures. The material of interest is selected to be a high-performance
unidirectional carbon fiber-reinforced polymer composite. The deep learning
framework contains two stacked fully-convolutional networks, namely, Generator
1 and Generator 2, trained sequentially. First, Generator 1 learns to translate
the microstructural geometry to the full-field post-failure stress
distribution. Then, Generator 2 learns to translate the output of Generator 1
to the failure pattern. A physics-informed loss function is also designed and
incorporated to further improve the performance of the proposed framework and
facilitate the validation process. In order to provide a sufficiently large
data set for training and validating the deep learning framework, 4500
microstructural representations are synthetically generated and simulated in an
efficient finite element framework. It is shown that the proposed deep learning
approach can effectively predict the composites' post-failure full-field stress
distribution and failure pattern, two of the most complex phenomena to simulate
in computational solid mechanics.
