6 citations · 18 across the 7 of their papers we have counts for
20 papers
Conditional Variational Autoencoder for Learned Image Reconstruction
Chen Zhang, Riccardo Barbano, Bangti Jin
Learned image reconstruction techniques using deep neural networks have recently gained popularity, and have delivered promising empirical results. However, most approaches focus o…
Recovering the Potential and Order in One-Dimensional Time-Fractional Diffusion with Unknown Initial Condition and Source
Bangti Jin, Zhi Zhou
This paper is concerned with an inverse problem of recovering a potential term and fractional order in a one-dimensional subdiffusion problem, which involves a Djrbashian-Caputo fr…
Reconstruction of a Space-Time Dependent Source in Subdiffusion Models via a Perturbation Approach
Bangti Jin, Yavar Kian, Zhi Zhou
In this article we study inverse problems of recovering a space-time dependent source component from the lateral boundary observation in a subidffusion model. The mathematical mode…
Recovery of the Order of Derivation for Fractional Diffusion Equations in an Unknown Medium
Bangti Jin, Yavar Kian
In this work, we investigate the recovery of a parameter in a diffusion process given by the order of derivation in time for a class of diffusion type equations, including both cla…
Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
Riccardo Barbano, Željko Kereta, Chen Zhang +3
Image reconstruction methods based on deep neural networks have shown outstanding performance, equalling or exceeding the state-of-the-art results of conventional approaches, but o…
Error Analysis of Finite Element Approximations of Diffusion Coefficient Identification for Elliptic and Parabolic Problems
Bangti Jin, Zhi Zhou
In this work, we present a novel error analysis for recovering a spatially dependent diffusion coefficient in an elliptic or parabolic problem. It is based on the standard regulari…