Abstract
In this work we propose to assess the application of deep neural networks as surrogate models for traditional differential models. Neural networks learning process will be guided not only by a training set -- as usual -- but also by adding constraints imposing characteristics from the phenomenon being modelled. The training set will be formed from samples coming from solutions of model pr…