  This paper proposes a novel Machine Learning-based approach to solve a
Poisson problem with mixed boundary conditions. Leveraging Graph Neural
Networks, we develop a model able to process unstructured grids with the
advantage of enforcing boundary conditions by design. By directly minimizing
the residual of the Poisson equation, the model attempts to learn the physics
of the problem without the need for exact solutions, in contrast to most
previous data-driven processes where the distance with the available solutions
is minimized.
