Extension and neural operator approximation of the electrical impedance tomography inverse map

• URL: http://arxiv.org/abs/2511.20361v1
• Date: Tue, 25 Nov 2025 14:43:13 GMT
• Title: Extension and neural operator approximation of the electrical impedance tomography inverse map
• Authors: Maarten V. de Hoop, Nikola B. Kovachki, Matti Lassas, Nicholas H. Nelsen,
• Abstract summary: This paper considers the problem of noise-robust neural operator approximation for the solution map of Caldern's inverse conductivity problem.<n>The boundary measurements are realized as a noisy perturbation of the Neumann-to-Dirichlet map's integral kernel.<n>The resulting extension shares the same stability properties as the original inverse map from kernels to conductivities, but is now amenable to neural operator approximation.
• Score: 15.065275438824008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper considers the problem of noise-robust neural operator approximation for the solution map of Calderón's inverse conductivity problem. In this continuum model of electrical impedance tomography (EIT), the boundary measurements are realized as a noisy perturbation of the Neumann-to-Dirichlet map's integral kernel. The theoretical analysis proceeds by extending the domain of the inversion operator to a Hilbert space of kernel functions. The resulting extension shares the same stability properties as the original inverse map from kernels to conductivities, but is now amenable to neural operator approximation. Numerical experiments demonstrate that Fourier neural operators excel at reconstructing infinite-dimensional piecewise constant and lognormal conductivities in noisy setups both within and beyond the theory's assumptions. The methodology developed in this paper for EIT exemplifies a broader strategy for addressing nonlinear inverse problems with a noise-aware operator learning framework.

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