  We consider the Dirichlet-to-Neumann operator and the direct and inverse
Calder\'on's mappings appearing in the Inverse Problem of recovering a smooth
bounded and positive isotropic conductivity of a material filling a smooth
bounded domain in space. Using deep learning techniques, we prove that these
mappings are rigorously approximated by DeepONets, infinite-dimensional
counterparts of standard artificial neural networks.
