J
Jun Zhang
Researcher at University of Kentucky
Publications - 199
Citations - 4299
Jun Zhang is an academic researcher from University of Kentucky. The author has contributed to research in topics: Multigrid method & Preconditioner. The author has an hindex of 37, co-authored 188 publications receiving 4031 citations. Previous affiliations of Jun Zhang include Southwest Petroleum University & University of Minnesota.
Papers
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High order ADI method for solving unsteady convection-diffusion problems
Samir Karaa,Jun Zhang +1 more
TL;DR: A high order alternating direction implicit (ADI) solution method for solving unsteady convection-diffusion problems and it is shown through a discrete Fourier analysis that the method is unconditionally stable for 2D problems.
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Incomplete LU preconditioning for large scale dense complex linear systems from electromagnetic wave scattering problems
TL;DR: Experimental results show that the ILU preconditionser reduces the number of BiCG iterations substantially, compared to the block diagonal preconditioner, and maintains the computational complexity of the MLFMA, and consequently reduces the total CPU time.
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Sparse inverse preconditioning of multilevel fast multipole algorithm for hybrid Integral equations in electromagnetics
TL;DR: This study shows that a good quality SAI preconditioner can be constructed by using the near part matrix numerically generated in the MLFMA and can reduce the number of Krylov iterations substantially.
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Comparison of Second- and Fourth-Order Discretizations for Multigrid Poisson Solvers
TL;DR: A compact high-order difference approximation with multigrid V-cycle algorithm to solve the two-dimensional Poisson equation with Dirichlet boundary conditions and is compared with the five-point formula to show the dramatic improvement in computed accuracy.
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Modeling and numerical simulation of bioheat transfer and biomechanics in soft tissue
TL;DR: A mathematical model describing the thermomechanical interactions in biological bodies at high temperature is proposed by treating the soft tissue in Biological bodies as a thermoporoelastic media and the proposed numerical techniques are efficient.