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The goal of this work is to develop deep learning numerical methods forsolving option XVA pricing problems given by non-linear PDE models. A novelstrategy for the treatment of the boundary conditions is proposed, which allowsto get rid of the heuristic choice of the weights for the different addendsthat appear in the loss function related to the training process. It is basedon defining the losses associated to the boundaries by means of the PDEs thatarise from substituting the related conditions into the model equation itself.Further, automatic differentiation is employed to obtain accurate approximationof the partial derivatives.

Boundary-safe PINNs extension: Application to non-linear parabolic PDEs in counterparty credit risk