293 citations · 1.8k across the 110 of their papers we have counts for
7 papers · 1 filter
Numerical Approximation of Stochastic Time-Fractional Diffusion
Bangti Jin, Yubin Yan, Zhi Zhou
We develop and analyze a numerical method for stochastic time-fractional diffusion driven by additive fractionally integrated Gaussian noise. The model involves two nonlocal terms…
Expectation Propagation for Poisson Data
Chen Zhang, Simon Arridge, Bangti Jin
The Poisson distribution arises naturally when dealing with data involving counts, and it has found many applications in inverse problems and imaging. In this work, we develop an a…
Subdiffusion with a time-dependent coefficient: analysis and numerical solution
Bangti Jin, Buyang Li, Zhi Zhou
In this work, a complete error analysis is presented for fully discrete solutions of the subdiffusion equation with a time-dependent diffusion coefficient, obtained by the Galerkin…
Linearized Reconstruction for Diffuse Optical Spectroscopic Imaging
Habib Ammari, Bangti Jin, Wenlong Zhang
In this paper, we present a novel reconstruction method for diffuse optical spectroscopic imaging with a commonly used tissue model of optical absorption and scattering. It is base…
Acousto-Electric Tomography with Total Variation Regularization
Bolaji James Adesokan, Bjørn Jensen, Bangti Jin +1
We study the numerical reconstruction problem in acousto-electric tomography of recovering the conductivity distribution in a bounded domain from interior power density data. We pr…
Numerical methods for time-fractional evolution equations with nonsmooth data: a concise overview
Bangti Jin, Raytcho Lazarov, Zhi Zhou
Over the past few decades, there has been substantial interest in evolution equations that involving a fractional-order derivative of order in time, due to their many s…
On the Regularizing Property of Stochastic Gradient Descent
Bangti Jin, Xiliang Lu
Stochastic gradient descent is one of the most successful approaches for solving large-scale problems, especially in machine learning and statistics. At each iteration, it employs…