4 papers
Constrained Neural Parameterization for Optimization in Function Spaces
Michael Hintermüller, Jianfeng Ning
We propose constrained neural parameterization schemes for several classes of constraints arising in optimization problems in function spaces. This is achieved by constructing smoo…
Deep Learning Based Reconstruction Methods for Electrical Impedance Tomography
Alexander Denker, Fabio Margotti, Jianfeng Ning +5
Electrical Impedance Tomography (EIT) is a powerful imaging modality widely used in medical diagnostics, industrial monitoring, and environmental studies. The EIT inverse problem i…
A Direct Sampling Method and Its Integration with Deep Learning for Inverse Scattering Problems with Phaseless Data
Jianfeng Ning, Fuqun Han, Jun Zou
We consider in this work an inverse acoustic scattering problem when only phaseless data is available. The inverse problem is highly nonlinear and ill-posed due to the lack of the…
Constructing probing functions for direct sampling methods for inverse scattering problems with limited-aperture data: finite space framework and deep probing network
Jianfeng Ning, Jun Zou
This work studies an inverse scattering problem when limited-aperture data are available that are from just one or a few incident fields. This inverse problem is highly ill-posed d…