Domain decomposition method of stochastic PDEs: a two-level scalable preconditioner

Domain decomposition method of stochastic PDEs: a two-level scalable preconditioner

A domain decomposition method of stochastic PDEs: An iterative solution techniques using a two-level scalable preconditioner

Recent advances in high performance computing systems and sensing technologies motivate computational simulations with extremely high resolution models with capabilities to quantify uncertainties for credible numerical predictions. A two-level domain decomposition method is reported in this investigation to devise a linear solver for the large-scale system in the Galerkin spectral stochastic fi...

Scalable Two-level Domain Decomposition Algorithms for Stochastic Systems

Recent advances of high performance computing systems permits extreme scale computational simulation with high resolution numerical models. For uncertainty quantification of such extreme scale simulations, the computational cost of the traditional Monte Carlo simulations (MCS) may become extremely high. As an efficient alternative to MCS, the intrusive spectral stochastic finite element method ...

Scalable Parallel Domain Decomposition Methods for Numerical Simulation of PDEs

This paper is concerned about scalable parallel domain decomposition methods for numerical simulation of PDEs. First, one level and two level scalable parallel domain decomposition methods which can be used to solve different equations, are introduced in detail, and then we explain Krylov subspace accelerator technique used to improve the convergence of the methods. Last, the results of some nu...

A preconditioner for the Schur complement domain decomposition method