55 citations · 59 across the 7 of their papers we have counts for
7 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…
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…
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…
Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches
Derick Nganyu Tanyu, Jianfeng Ning, Andreas Hauptmann +2
Electrical Impedance Tomography (EIT) is a powerful imaging technique with diverse applications, e.g., medical diagnosis, industrial monitoring, and environmental studies. The EIT…
A Direct Sampling-Based Deep Learning Approach for Inverse Medium Scattering Problems
Jianfeng Ning, Fuqun Han, Jun Zou
In this work, we focus on the inverse medium scattering problem (IMSP), which aims to recover unknown scatterers based on measured scattered data. Motivated by the efficient direct…
Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems
Derick Nganyu Tanyu, Jianfeng Ning, Tom Freudenberg +4
Recent years have witnessed a growth in mathematics for deep learning--which seeks a deeper understanding of the concepts of deep learning with mathematics and explores how to make…