  We rewrite the numerical ansatz of the Method of Auxiliary Sources (MAS),
typically used in computational electromagnetics, as a neural network, i.e. as
a composed function of linear and activation layers. MAS is a numerical method
for Partial Differential Equations (PDEs) that employs point sources, which are
also exact solutions of the considered PDE, as radial basis functions to match
a given boundary condition. In the framework of neural networks we rely on
optimization algorithms such as Adam to train MAS and find both its optimal
coefficients and positions of the central singularities of the sources. In this
work we also show that the MAS ansatz trained as a neural network can be used,
in the case of an unknown function with a central singularity, to detect the
position of such singularity.
