  The dynamics of materials failure is one of the most critical phenomena in a
range of scientific and engineering fields, from healthcare to structural
materials to transportation. In this paper we propose a specially designed deep
neural network, DyFraNet, which can predict dynamic fracture behaviors by
identifying a complete history of fracture propagation - from cracking onset,
as a crack grows through the material, modeled as a series of frames evolving
over time and dependent on each other. Furthermore, this model can not only
forecast future fracture processes but also backcast to elucidate the past
fracture history. In this scenario, once provided with the outcome of a
fracture event, the model will elucidate past events that led to this state and
will predict the future evolution of the failure process. By comparing the
predicted results with atomistic-level simulations and theory, we show that
DyFraNet can capture dynamic fracture mechanics by accurately predicting how
cracks develop over time, including measures such as the crack speed, as well
as when cracks become unstable. We use GradCAM to interpret how DyFraNet
perceives the relationship between geometric conditions and fracture dynamics
and we find DyFraNet pays special attention to the areas around crack tips,
which have a critical influence in the early stage of fracture propagation. In
later stages, the model pays increased attention to the existing or newly
formed damage distribution in the material. The proposed approach offers
significant potential to accelerate the exploration of the dynamics in material
design against fracture failures and can be beneficially adapted for all kinds
of dynamical engineering problems.
