2010
DOI: 10.1002/num.20609
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Some numerical techniques for solving elliptic interface problems

Abstract: Many physical phenomena can be modeled by partial differential equations with singularities and interfaces. The standard finite difference and finite element methods may not be successful in giving satisfactory numerical results for such problems. Hence, many new methods have been developed. Some of them are developed with the modifications in the standard methods, so that they can deal with the discontinuities and the singularities. In this article, a survey has been done on some recent efficient techniques t… Show more

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Cited by 14 publications
(6 citation statements)
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“…The original IIM adds additional nodes to numerical stencil, leading to a non-symmetric matrix. This nonsymmetric matrix reduces the numbers of efficient numerical solvers to be used and convergence is not always guaranteed (Kumar, Joshi 2012b). This increases the computational cost of solving this matrix.…”
Section: A Decomposed Immersed Interface Methods For Elliptic Interfacmentioning
confidence: 98%
“…The original IIM adds additional nodes to numerical stencil, leading to a non-symmetric matrix. This nonsymmetric matrix reduces the numbers of efficient numerical solvers to be used and convergence is not always guaranteed (Kumar, Joshi 2012b). This increases the computational cost of solving this matrix.…”
Section: A Decomposed Immersed Interface Methods For Elliptic Interfacmentioning
confidence: 98%
“…J. H. He proposed the variational iteration method [4,5] to solve linear and nonlinear differential equations [6][7][8][9] using an iterative scheme. He modified the general Lagrange multiplier method and constructed an iterative sequence of functions which converges to the exact solution generally.…”
Section: Variational Iteration Methodsmentioning
confidence: 99%
“…This is a sparse linear system in Z, which can be solved using linear conjugate gradient method. For various preconditioned CG approaches, see [53,33,34,35,36,37,38,42,48,52,41,43,44,45,46,47,49,50,51]. Problem (9) has only one term involving S. Hence, the optimization problem over S reduces to…”
Section: Convex Optimization Problemmentioning
confidence: 99%