IFISS

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 229 articles , 1 standard article )

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  1. Badahmane, A.; Bentbib, A. H.; Sadok, H.: Preconditioned Krylov subspace and GMRHSS iteration methods for solving the nonsymmetric saddle point problems (2020)
  2. Cao, Yang: A block positive-semidefinite splitting preconditioner for generalized saddle point linear systems (2020)
  3. Cao, Yang; Shi, Zhen-Quan; Shi, Quan: Regularized DPSS preconditioners for generalized saddle point linear systems (2020)
  4. Chen, Chen; Liao, Qifeng: ANOVA Gaussian process modeling for high-dimensional stochastic computational models (2020)
  5. Cheng, Guo; Li, Ji-Cheng: A relaxed upper and lower triangular splitting preconditioner for the linearized Navier-Stokes equation (2020)
  6. Elman, Howard C.; Su, Tengfei: A low-rank solver for the stochastic unsteady Navier-Stokes problem (2020)
  7. Fan, Hongtao; Zheng, Bing: Modified SIMPLE preconditioners for saddle point problems from steady incompressible Navier-Stokes equations (2020)
  8. Gao, Wen-Li; Li, Xi-An; Lu, Xin-Ming: On quasi shift-splitting iteration method for a class of saddle point problems (2020)
  9. 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)
  10. Li, Rui-Xia; Zhang, Guo-Feng; Liang, Zhao-Zheng: Fast solver of optimal control problems constrained by Ohta-Kawasaki equations (2020)
  11. Liu, Jun; Pearson, John W.: Parameter-robust preconditioning for the optimal control of the wave equation (2020)
  12. 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)
  13. Masoudi, Mohsen; Salkuyeh, Davod Khojasteh: An extension of the positive-definite and skew-Hermitian splitting method for preconditioning of generalized saddle point problems (2020)
  14. Rahimian, Maryam; Salkuyeh, Davod Khojasteh: Spectral analysis of the MGSS preconditioner for singular saddle point problems (2020)
  15. Salkuyeh, Davod Khojasteh: Shifted skew-symmetric/skew-symmetric splitting method and its application to generalized saddle point problems (2020)
  16. Sun, Zhen-Wei; Wang, Li: ERHSS iteration method for PDE optimal control problem (2020)
  17. Tang, Kejun; Liao, Qifeng: Rank adaptive tensor recovery based model reduction for partial differential equations with high-dimensional random inputs (2020)
  18. Wu, Shu-Lin; Liu, Jun: A parallel-in-time block-circulant preconditioner for optimal control of wave equations (2020)
  19. Yang, Xi: A fast null-space method for the unsteady Stokes equations (2020)
  20. Zhang, Ju-Li: On the regularization matrix of the regularized DPSS preconditioner for non-Hermitian saddle-point problems (2020)

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