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Found 2 papers, 1 papers with code
Domain independent post-processing with graph U-nets: Applications to Electrical Impedance Tomographic Imaging
We demonstrate effectiveness and flexibility of the graph U-Net for improving reconstructions from electrical impedance tomographic (EIT) measurements, a nonlinear and highly ill-posed inverse problem.
Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems
This gives rise to the proposed iterative Graph Convolutional Newton-type Method (GCNM), which includes the forward model in the solution of the inverse problem, while all updates are directly computed by the network on the problem specific mesh.
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