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• W2948801778 abstract "This paper introduces a new approach for solving electrical impedance tomography (EIT) problems using deep neural networks. The mathematical problem of EIT is to invert the electrical conductivity from the Dirichlet-to-Neumann (DtN) map. Both the forward map from the electrical conductivity to the DtN map and the inverse map are high-dimensional and nonlinear. Motivated by the linear perturbative analysis of the forward map and based on a numerically low-rank property, we propose compact neural network architectures for the forward and inverse maps for both 2D and 3D problems. Numerical results demonstrate the efficiency of the proposed neural networks." @default.
• W2948801778 created "2019-06-14" @default.
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• W2948801778 date "2020-03-01" @default.
• W2948801778 modified "2023-10-02" @default.
• W2948801778 title "Solving electrical impedance tomography with deep learning" @default.
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• W2948801778 doi "https://doi.org/10.1016/j.jcp.2019.109119" @default.
• W2948801778 hasPublicationYear "2020" @default.
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