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Current density impedance imaging with PINNs by Duan, Chenguang; Jiao, Yuling; Lu, Xiliang; Yang, Jerry Zhijian is a scholarly article available to read on EtoBox.

What is Current density impedance imaging with PINNs about?

In this paper, we introduce CDII-PINNs, a computationally efficient method for solving CDII using PINNs in the framework of Tikhonov regularization. This method constructs a physics-informed loss function by merging the regularized least-squares output functional with an underlying differential equation, which describes the relationship between the conductivity and voltage. A pair of neural networks representing the conductivity and voltage, respectively, are coupled by this loss function. Then, minimizing the loss function provides a reconstruction. A rigorous theoretical guarantee is provided. We give an error analysis for CDII-PINNs and establish a convergence rate, based on prior selected neural network parameters in terms of the number of samples. The numerical simulations demonstrate that CDII-PINNs are efficient, accurate and robust to noise levels ranging from $1\%$ to $20\%$.

Author

Duan, Chenguang; Jiao, Yuling; Lu, Xiliang; Yang, Jerry Zhijian

Published

2023

Language

EN