1 citations · 1 across the 7 of their papers we have counts for
8 papers
Early stopping of stochastic variance reduced gradient for linear inverse problems by the discrepancy principle
Bangti Jin, Zehui Zhou
Stochastic variance reduced gradient (SVRG) is a variant of stochastic gradient descent and is a promising iterative method for solving large-scale inverse problems. Nevertheless,…
Error Analysis of the Inverse Conductivity Problem with Scattered Measurements
Bangti Jin, Qimeng Quan, Wenlong Zhang
In this work, we investigate the inverse problem of recovering the conductivity coefficient in an elliptic equation from noisy measurements collected at finitely many deterministic…
Solving Inverse Acoustic Obstacle Scattering Problem with Phaseless Far-Field Measurement Using Deep Neural Network Surrogates
Yuxin Fan, Jiho Hong, Bangti Jin
In this work, we investigate the use of deep neural networks (DNNs) as surrogates for solving the inverse acoustic scattering problem of recovering a sound-soft obstacle from phase…
Unique and Stable Recovery of Space-Variable Order in Multidimensional Subdiffusion
Jiho Hong, Bangti Jin, Yavar Kian
In this work we investigate the unique identifiability and stable recovery of a spatially dependent variable-order in the subdiffusion model from the boundary flux measurement. We…
Direct Algorithms for Reconstructing Small Conductivity Inclusions in Subdiffusion
Jiho Hong, Bangti Jin, Zhizhang Wu
The subdiffusion model that involves a Caputo fractional derivative in time is widely used to describe anomalously slow diffusion processes. In this work we aim at recovering the l…
Theory and Fast Learned Solver for -TV Regularization
Xinling Liu, Jianjun Wang, Bangti Jin
The and total variation (TV) penalties have been used successfully in many areas, and the combination of the and TV penalties can lead to further improved perform…
Early Stopping of Untrained Convolutional Neural Networks
Tim Jahn, Bangti Jin
In recent years, new regularization methods based on (deep) neural networks have shown very promising empirical performance for the numerical solution of ill-posed problems, e.g.,…
Solving Inverse Obstacle Scattering Problem with Latent Surface Representations
Junqing Chen, Bangti Jin, Haibo Liu
We propose a novel iterative numerical method to solve the three-dimensional inverse obstacle scattering problem of recovering the shape of the obstacle from far-field measurements…