  We explore the potential of the deep Ritz method to learn complex fracture
processes such as quasistatic crack nucleation, propagation, kinking,
branching, and coalescence within the unified variational framework of
phase-field modeling of brittle fracture. We elucidate the challenges related
to the neural-network-based approximation of the energy landscape, and the
ability of an optimization approach to reach the correct energy minimum, and we
discuss the choices in the construction and training of the neural network
which prove to be critical to accurately and efficiently capture all the
relevant fracture phenomena. The developed method is applied to several
benchmark problems and the results are shown to be in qualitative and
quantitative agreement with the finite element solution. The robustness of the
approach is tested by using neural networks with different initializations.
