2015.42: Efficient adaptive stochastic Galerkin methods for parametric operator equations
2015.42: Alex Bespalov and David Silvester (2015) Efficient adaptive stochastic Galerkin methods for parametric operator equations.
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This paper is concerned with the design and implementation of efficient solution algorithms for elliptic PDE problems with correlated random data. The energy orthogonality that is built into stochastic Galerkin approximations is cleverly exploited to give an innovative energy error estimation strategy that utilizes the tensor product structure of the approximation space. An associated error estimator is constructed and shown theoretically and numerically to be an effective mechanism for driving an adaptive refinement process. The codes used in the numerical studies are available online.
|Item Type:||MIMS Preprint|
|Uncontrolled Keywords:||stochastic Galerkin methods, stochastic finite elements, PDEs with random data, error estimation, a posteriori error analysis, adaptive methods, parametric operator equations|
|Subjects:||MSC 2000 > 35 Partial differential equations|
MSC 2000 > 65 Numerical analysis
|Deposited By:||professor david silvester|
|Deposited On:||21 June 2015|