  The goal of this work is to develop deep learning numerical methods for
solving option XVA pricing problems given by non-linear PDE models. A novel
strategy for the treatment of the boundary conditions is proposed, which allows
to get rid of the heuristic choice of the weights for the different addends
that appear in the loss function related to the training process. It is based
on defining the losses associated to the boundaries by means of the PDEs that
arise from substituting the related conditions into the model equation itself.
Further, automatic differentiation is employed to obtain accurate approximation
of the partial derivatives.
