activity
20182021
most citedA data-driven approach for multiscale elliptic PDEs with random coefficients based on intrinsic dimension reduction

1 citations · 1 across the 2 of their papers we have counts for

collaborators

10 papers

math.NA2021

A data-driven and model-based accelerated Hamiltonian Monte Carlo method for Bayesian elliptic inverse problems

Sijing Li, Cheng Zhang, Zhiwen Zhang +1

In this paper, we consider a Bayesian inverse problem modeled by elliptic partial differential equations (PDEs). Specifically, we propose a data-driven and model-based approach to…

math.NA2020

Quantitative PAT with simplified approximation

Hongkai Zhao, Yimin Zhong

The photoacoustic tomography (PAT) is a hybrid modality that combines the optics and acoustics to obtain high resolution and high contrast imaging of heterogeneous media. In this w…

cs.CV2020

A Dual Iterative Refinement Method for Non-rigid Shape Matching

Rui Xiang, Rongjie Lai, Hongkai Zhao

In this work, a simple and efficient dual iterative refinement (DIR) method is proposed for dense correspondence between two nearly isometric shapes. The key idea is to use dual in…

cs.CV2020

Efficient and Robust Shape Correspondence via Sparsity-Enforced Quadratic Assignment

Rui Xiang, Rongjie Lai, Hongkai Zhao

In this work, we introduce a novel local pairwise descriptor and then develop a simple, effective iterative method to solve the resulting quadratic assignment through sparsity cont…

math.NA2020

A fast algorithm for time-dependent radiative transport equation based on integral formulation

Hongkai Zhao, Yimin Zhong

In this work, we introduce a fast numerical algorithm to solve the time-dependent radiative transport equation (RTE). Our method uses the integral formulation of RTE and applies th…

math.OC2019

Solving Phase Retrieval via Graph Projection Splitting

Ji Li, Hongkai Zhao

Phase retrieval with prior information can be cast as a nonsmooth and nonconvex optimization problem. We solve the problem by graph projection splitting (GPS), where the two proxim…