  Despite the increasing importance of strain localization modeling (e.g.,
failure analysis) in computer-aided engineering, there is a lack of effective
approaches to consistently modeling related material behaviors across multiple
length scales. We aim to address this gap within the framework of deep material
networks (DMN) - a physics-based machine learning model with embedded mechanics
in the building blocks. A new cell division scheme is proposed to track the
scale transition through the network, and its consistency is ensured by the
physics of fitting parameters. Essentially, each microscale node in the bottom
layer is described by an ellipsoidal cell with its dimensions back-propagated
from the macroscale material point. New crack surfaces in the cell are modeled
by enriching cohesive layers, and failure algorithms are developed for crack
initiation and evolution in the implicit DMN analysis. Besides single material
point studies, we apply the multiscale model to concurrent multiscale
simulations for the dynamic crush of a particle-reinforced composite tube and
various tests on carbon fiber reinforced polymer composites. For the latter,
experimental validations on an off-axis tensile test specimen are also
provided.
