  Hybrid Optimization Software Suite (HOSS), which is a combined
finite-discrete element method (FDEM), is one of the advanced approaches to
simulating high-fidelity fracture and fragmentation processes but the
application of pure HOSS simulation is computationally expensive. At the same
time, machine learning methods, shown tremendous success in several scientific
problems, are increasingly being considered promising alternatives to
physics-based models in the scientific domains. Thus, our goal in this work is
to build a new data-driven methodology to reconstruct the crack fracture
accurately in the spatial and temporal fields. We leverage physical constraints
to regularize the fracture propagation in the long-term reconstruction. In
addition, we introduce perceptual loss and several extra pure machine learning
optimization approaches to improve the reconstruction performance of fracture
data further. We demonstrate the effectiveness of our proposed method through
both extrapolation and interpolation experiments. The results confirm that our
proposed method can reconstruct high-fidelity fracture data over space and time
in terms of pixel-wise reconstruction error and structural similarity. Visual
comparisons also show promising results in long-term
