  We use a Convolutional Recurrent Neural Network approach to learn
morphological evolution driven by surface diffusion. To this aim we first
produce a training set using phase field simulations. Intentionally, we insert
in such a set only relatively simple, isolated shapes. After proper data
augmentation, training and validation, the model is shown to correctly predict
also the evolution of previously unobserved morphologies and to have learned
the correct scaling of the evolution time with size. Importantly, we quantify
prediction uncertainties based on a bootstrap-aggregation procedure. The latter
proved to be fundamental in pointing out high uncertainties when applying the
model to more complex initial conditions (e.g. leading to splitting of high
aspect-ratio individual structures). Automatic smart-augmentation of the
training set and design of a hybrid simulation method are discussed.

Computational Physics;Mesoscale and Nanoscale Physics;Materials Science