The Calderón's problem via DeepONets
We consider the Dirichlet-to-Neumann operator and the Calderón's mapping 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 this map is rigorously approximated by DeepONets, infinite-dimensional counterparts of standard artificial neural networks.
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