An Adaptive Stochastic Galerkin Tensor Train Discretization for Randomly Perturbed Domains

DOI10.1137/19M1246080zbMath1447.65137arXiv1902.07753OpenAlexW3080922984MaRDI QIDQ5119643

Martin Eigel, Manuel Marschall, Michael D. Multerer

Publication date: 31 August 2020

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.07753

zbMATH Keywords

adaptive methodsuncertainty quantificationlow-rankpartial differential equations with random coefficientsrandom domaintensor train

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Stability and convergence of numerical methods for boundary value problems involving PDEs (65N12) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) Numerical solutions to equations with linear operators (65J10) PDEs with randomness, stochastic partial differential equations (35R60) Random linear operators (47B80) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solution of discretized equations for boundary value problems involving PDEs (65N22)

Related Items

Adaptive Nonintrusive Reconstruction of Solutions to High-Dimensional Parametric PDEs, Stochastic Approximation for Optimization in Shape Spaces, Tensor train based isogeometric analysis for PDE approximation on parameter dependent geometries

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