Machine learning enhanced electrical impedance tomography for 2D materials

DOI10.1088/1361-6420/AC7743zbMath1502.78019OpenAlexW4281676262MaRDI QIDQ5089407

Ivo Mihov, Andrey Kretinin, Adam Coxson, William R. B. Lionheart, Frederik Brooke Barnes, David Sanderson, Artem Mishchenko, Ziwei Wang, Qian Yang, Vasil Avramov, Ciaran Mullan, Sergey Slizovskiy, Ivan V. Timokhin

Publication date: 19 July 2022

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1088/1361-6420/ac7743

zbMATH Keywords

machine learningelectrical impedance tomographymeasurement optimization

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) PDEs in connection with optics and electromagnetic theory (35Q60) Biomedical imaging and signal processing (92C55) Ill-posed problems for PDEs (35R25) Inverse problems for PDEs (35R30) Diffraction, scattering (78A45) Composite media; random media in optics and electromagnetic theory (78A48) Inverse problems (including inverse scattering) in optics and electromagnetic theory (78A46) Statistical mechanics of nanostructures and nanoparticles (82D80)

Uses Software

• TensorFlow
• Adam
• pyEIT

Cites Work

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• Review on electrical impedance tomography: artificial intelligence methods and its applications
• Solving electrical impedance tomography with deep learning
• Arts of electrical impedance tomographic sensing
• Existence and Uniqueness for Electrode Models for Electric Current Computed Tomography
• Electrical Impedance Tomography
• Classification of stroke using neural networks in electrical impedance tomography

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