For the particular case of adapting the highly non-linear physics of electrochemical models into a complex data driven architecture, in the specific case of working with neural networks, is to inform the loss functions with the residual of the physical equations. This particular approach has been previously discussed in a study of adapting deep learning (DL) functionalities to solve problems involving PDEs .