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• W3030487003 abstract "Objective: In this work, we study the application of the neural network based supervised descent method (NN-SDM) for 2D electrical impedance tomography. Approach: NN-SDM contains two stages: o_ine training and online prediction. In the o_ine stage, neural networks are iteratively applied to learn a sequence of descent directions for minimizing the objective function, where the training data set is generated in advance according to prior information or historical data; in the online stage, the trained neural networks are directly used to predict the descent directions. Main results: Numerical and experimental results are reported to assess the e_ciency and accuracy of NN-SDM for both model-based and pixel-based inversions. In addition, the performance of NN-SDM is also compared with linear SDM (LSDM), end-to-end neural network (E2E-NN) and Gauss-Newton method (GN). The results demonstrate that NN-SDM achieves faster convergence than LSDM and GN, and stronger generalization ability than E2E-NN. Signi_cance: NN-SDM combines the strong nonlinear _tting ability of neural network and good generalization capability of the supervised descent method (SDM), which also provides good exibility to incorporate prior information and accelerates the convergence of iteration." @default.
• W3030487003 created "2020-06-05" @default.
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• W3030487003 date "2020-08-11" @default.
• W3030487003 modified "2023-10-18" @default.
• W3030487003 title "Neural network-based supervised descent method for 2D electrical impedance tomography" @default.
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• W3030487003 doi "https://doi.org/10.1088/1361-6579/ab9871" @default.
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