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Conditional spatial finite-element rate for stochastic Galerkin
Statement
Assume the hypotheses of the stochastic Galerkin theorem with , , and suppose the exact solution has the exact finite expansion for integers and . Choose a fixed reference finite element whose polynomial space contains and whose unisolvent degrees of freedom are bounded on of the reference cell; let its reference interpolant reproduce and define local interpolants by affine pullback as in Local interpolation estimates on shape-regular affine meshes. Let be a shape-regular family of matching affine simplicial meshes as in Matching affine simplicial meshes and shape regularity, with maximum cell diameter , and assume for each mesh that a globally conforming spatial interpolant maps these coefficients into its finite-element space, restricts cellwise to those local interpolants, and preserves the zero boundary condition. These are the generic bounded-degree-of-freedom hypotheses of the local estimate. Then for the stochastic Galerkin solution , The regularity and finite-expansion assumptions are explicit; this is not a general spatial rate for arbitrary random solutions.
Facts & Assumptions
Given: Uniform ellipticity, the exact finite expansion and coefficient regularity above, a shape-regular mesh, and a globally conforming interpolant with the stated cell restrictions.
Stochastic Galerkin coercivity and Cea quasi-optimality gives the Galerkin error bounded by times best approximation in the tensor space.
Local interpolation estimates on shape-regular affine meshes with , , the fixed reference space containing , bounded degrees of freedom on , and gives . The generic theorem uses the boundedness hypothesis directly; the corollary does not infer nodal boundedness from point evaluation.
Independent polynomial-chaos tensor products are orthonormal gives the orthonormal finite random basis.
Proof
Define . Each interpolated coefficient belongs to the spatial finite-element subspace, so . By [F3], orthogonality in the random variable gives
For each coefficient and cell , [F2] gives . Square, sum over the mesh cells, and use to obtain . This uses the assumed cellwise conformity and disjoint cell interiors.
Square the bounds from step 1.2, sum over the finite , and use step 1.1 to get . Then [F1] bounds the Galerkin error by times this particular approximation error, proving the claim.
Depends on
- Stochastic Galerkin coercivity and Cea quasi-optimality
- Local interpolation estimates on shape-regular affine meshes
- Matching affine simplicial meshes and shape regularity
- Independent polynomial-chaos tensor products are orthonormal
- Uniformly elliptic affine random diffusion model
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
Used by
Nothing in the library uses this result yet.
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Sources
- Ivo Babuška, Fabio Nobile, and Raúl Tempone, A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data, §§1–2 (standard reference, not scraped)
- Bruno Sudret, Polynomial Chaos Expansions and Stochastic Finite Element Methods, Chapter 6 (standard reference, not scraped)