H
Hongkai Zhao
Researcher at University of California, Irvine
Publications - 157
Citations - 9983
Hongkai Zhao is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Inverse problem & Discretization. The author has an hindex of 41, co-authored 153 publications receiving 9211 citations. Previous affiliations of Hongkai Zhao include University of California, Los Angeles & Duke University.
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Journal ArticleDOI
A Variational Level Set Approach to Multiphase Motion
TL;DR: In this paper, a coupled level set method for the motion of multiple junctions (of, e.g., solid, liquid, and grain boundaries), which follows the gradient flow for an energy functional consisting of surface tension and bulk energies, is developed.
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Regular Article: A PDE-Based Fast Local Level Set Method
TL;DR: A fast method to localize thelevel set method of Osher and Sethian and address two important issues that are intrinsic to the level set method, which reduces the computational effort by one order of magnitude, works in as much generality as the original one, and is conceptually simple and easy to implement.
Journal ArticleDOI
A fast sweeping method for Eikonal equations
TL;DR: Monotonicity and stability properties of the fast sweeping algorithm are proven and it is shown that 2 n Gauss-Seidel iterations is enough for the distance function in n dimensions.
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Fast Sweeping Algorithms for a Class of Hamilton--Jacobi Equations
TL;DR: A Godunov-type numerical flux is derived for the class of strictly convex, homogeneous Hamiltonians that includes H(p,q) and it is shown that convergence after a few iterations, even in rather difficult cases, is indicated.
Proceedings ArticleDOI
Fast surface reconstruction using the level set method
TL;DR: The level set method and fast sweeping and tagging methods are used to reconstruct surfaces from a scattered data set and the reconstructed surface is smoother than piecewise linear and has a natural scaling in the regularization that allows varying flexibility according to the local sampling density.