Algorithm 866: IFISS, a Matlab toolbox for modelling incompressible flow. IFISS is a graphical Matlab package for the interactive numerical study of incompressible flow problems. It includes algorithms for discretization by mixed finite element methods and a posteriori error estimation of the computed solutions. The package can also be used as a computational laboratory for experimenting with state-of-the-art preconditioned iterative solvers for the discrete linear equation systems that arise in incompressible flow modelling. A unique feature of the package is its comprehensive nature; for each problem addressed, it enables the study of both discretization and iterative solution algorithms as well as the interaction between the two and the resulting effect on overall efficiency. (Source: http://dl.acm.org/)

References in zbMATH (referenced in 213 articles , 1 standard article )

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  1. Cao, Yang: A block positive-semidefinite splitting preconditioner for generalized saddle point linear systems (2020)
  2. Elman, Howard C.; Su, Tengfei: A low-rank solver for the stochastic unsteady Navier-Stokes problem (2020)
  3. Fan, Hongtao; Zheng, Bing: Modified SIMPLE preconditioners for saddle point problems from steady incompressible Navier-Stokes equations (2020)
  4. Liang, Zhao-Zheng; Axelsson, Owe; Zhang, Guo-Feng: Efficient iterative solvers for a complex valued two-by-two block linear system with application to parabolic optimal control problems (2020)
  5. Liu, Jun; Pearson, John W.: Parameter-robust preconditioning for the optimal control of the wave equation (2020)
  6. Li, Ya-Jing; Zhu, Xin-Yun; Fan, Hong-Tao: Relaxed block upper-lower triangular preconditioner for generalized saddle point problems from the incompressible Navier-Stokes equations (2020)
  7. Masoudi, Mohsen; Salkuyeh, Davod Khojasteh: An extension of the positive-definite and skew-Hermitian splitting method for preconditioning of generalized saddle point problems (2020)
  8. Wu, Shu-Lin; Liu, Jun: A parallel-in-time block-circulant preconditioner for optimal control of wave equations (2020)
  9. Zheng, Bing; Lv, Peng: Structured backward error analysis for generalized saddle point problems (2020)
  10. Axelsson, Owe; Liang, Zhao-Zheng: Parameter modified versions of preconditioning and iterative inner product free refinement methods for two-by-two block matrices (2019)
  11. Bootland, Niall; Bentley, Alistair; Kees, Christopher; Wathen, Andrew: Preconditioners for two-phase incompressible Navier-Stokes flow (2019)
  12. Camps, Daan; Meerbergen, Karl; Vandebril, Raf: A rational QZ method (2019)
  13. Camps, Daan; Meerbergen, Karl; Vandebril, Raf: An implicit filter for rational Krylov using core transformations (2019)
  14. Cao, Yang; Li, Sen: Block triangular preconditioners based on symmetric-triangular decomposition for generalized saddle point problems (2019)
  15. Elman, Howard C.; Su, Tengfei: Low-rank solution methods for stochastic eigenvalue problems (2019)
  16. Fan, Hongtao; Zhu, Xinyun; Li, Yajing; Zheng, Bing: A variant of relaxed triangular splitting preconditioners for generalized saddle point problems from Navier-Stokes equations (2019)
  17. Farrell, Patrick E.; Mitchell, Lawrence; Wechsung, Florian: An augmented Lagrangian preconditioner for the 3D stationary incompressible Navier-Stokes equations at High Reynolds number (2019)
  18. Gopal, Abinand; Trefethen, Lloyd N.: Solving Laplace problems with corner singularities via rational functions (2019)
  19. Grigori, Laura; Niu, Qiang; Xu, Yingxiang: Stabilized dimensional factorization preconditioner for solving incompressible Navier-Stokes equations (2019)
  20. Khan, Arbaz; Powell, Catherine E.; Silvester, David J.: Robust preconditioning for stochastic Galerkin formulations of parameter-dependent nearly incompressible elasticity equations (2019)

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Further publications can be found at: http://www.ma.man.ac.uk/~djs/ifiss/publist.html