Weak convergence of Galerkin approximations for fractional elliptic stochastic PDEs with spatial white noise

DOI10.1007/s10543-018-0719-8zbMath1405.65147arXiv1711.05188OpenAlexW2769521397WikidataQ60580261 ScholiaQ60580261MaRDI QIDQ1631188

Kristin Kirchner, Mihály Kovács, David Bolin

Publication date: 5 December 2018

Published in: BIT (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1711.05188

zbMATH Keywords

weak convergencespatial statisticsstochastic partial differential equationsfinite element methodsGaussian white noiseGalerkin methodsfractional operatorsMatérn covariances

Mathematics Subject Classification ID

Related Items (15)

Surface Finite Element Approximation of Spherical Whittle--Matérn Gaussian Random Fields ⋮ Limited‐memory polynomial methods for large‐scale matrix functions ⋮ Galerkin-Chebyshev approximation of Gaussian random fields on compact Riemannian manifolds ⋮ A unified view of some numerical methods for fractional diffusion ⋮ Multilevel approximation of Gaussian random fields: Fast simulation ⋮ Equivalence of measures and asymptotically optimal linear prediction for Gaussian random fields with fractional-order covariance operators ⋮ Efficient White Noise Sampling and Coupling for Multilevel Monte Carlo with Nonnested Meshes ⋮ Regularity and convergence analysis in Sobolev and Hölder spaces for generalized Whittle-Matérn fields ⋮ Fast sampling of parameterised Gaussian random fields ⋮ The Rational SPDE Approach for Gaussian Random Fields With General Smoothness ⋮ A Statistical Framework for Generating Microstructures of Two-Phase Random Materials: Application to Fatigue Analysis ⋮ Analysis of Boundary Effects on PDE-Based Sampling of Whittle--Matérn Random Fields ⋮ Weak convergence of fully discrete finite element approximations of semilinear hyperbolic SPDE with additive noise ⋮ The SPDE approach to Matérn fields: graph representations ⋮ Graph-based prior and forward models for inverse problems on manifolds with boundaries

Uses Software

• GMRFLib

Cites Work

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