  Despite the increasing importance of strain localization modeling (e.g.,
failure analysis) in computer-aided engineering, there is a lack of effective
approaches to capturing relevant material behaviors consistently across
multiple length scales. We aim to address this gap within the framework of deep
material networks (DMN) -- a 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 studies on a
single material point, 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.
