  We develop an artificial neural network (ANN) approach to classify simulated
signals corrsponding to the semi-classical description of Bloch oscillations in
pristine graphene. After the ANN is properly trained, we consider the inverse
problem of Bloch oscillations (BO),namely, a new signal is classified according
to the external electric field strength oriented along either the zig-zag or
arm-chair edges of the graphene membrane, with a correct classification that
ranges from 82.6% to 99.3% depending on the accuracy of the predicted electric
field. This approach can be improved depending on the time spent in training
the network and the computational power available. Findings in this work can be
straightforwardly extended to a variety of Dirac-Weyl materials.
