Cascaded Network for Inversion of Electrical Conductivity in High Noise Environment

In this paper, a cascade network is proposed to reconstruct the electrical conductivity through the electrical fields, presenting great computing efficiency and high accuracy. Benefit from the unsupervised U-net, the framework is applicable to high noise environment. Compared to traditional algorithms, the proposed physics informed neural networks (PINN) avoids meshing and iterative calculation. Therefore, it can be used to predict the conductivity distribution of objects with complex geometric shapes simultaneously. We believe this work will be benefit to fields related to electrical conductivity inversion problems.