General elliptic equations with spatially discontinuous diffusion coefficients may be used as a simplified model for subsurface flow in heterogeneous or fractured porous media. In such a model, data sparsity and measurement errors are often taken into account by a randomization of the diffusion coefficient of the elliptic equation which reveals the necessity of the construction of flexible, spatially discontinuous random fields. Subordinated Gaussian random fields are random functions on higher dimensional parameter domains with discontinuous sample paths and great distributional flexibility. In the present work, we consider a random elliptic partial differential equation (PDE) where the discontinuous subordinated Gaussian random fields occur in the diffusion coefficient. Problem specific multilevel Monte Carlo (MLMC) Finite Element methods are constructed to approximate the mean of the solution to the random elliptic PDE. We prove a-priori convergence of a standard MLMC estimator and a modified MLMC—control variate estimator and validate our results in various numerical examples.
%0 Journal Article
%1 merkle2022multilevel
%A Merkle, Robin
%A Barth, Andrea
%D 2022
%I Springer
%J BIT Numerical Mathematics
%K PN5-4 PN5 EXC2075 selected
%P 1279–1317
%R 10.1007/s10543-022-00912-4
%T Multilevel Monte Carlo estimators for elliptic PDEs with Lévy-type diffusion coefficient
%U https://doi.org/10.1007/s10543-022-00912-4
%V 62
%X General elliptic equations with spatially discontinuous diffusion coefficients may be used as a simplified model for subsurface flow in heterogeneous or fractured porous media. In such a model, data sparsity and measurement errors are often taken into account by a randomization of the diffusion coefficient of the elliptic equation which reveals the necessity of the construction of flexible, spatially discontinuous random fields. Subordinated Gaussian random fields are random functions on higher dimensional parameter domains with discontinuous sample paths and great distributional flexibility. In the present work, we consider a random elliptic partial differential equation (PDE) where the discontinuous subordinated Gaussian random fields occur in the diffusion coefficient. Problem specific multilevel Monte Carlo (MLMC) Finite Element methods are constructed to approximate the mean of the solution to the random elliptic PDE. We prove a-priori convergence of a standard MLMC estimator and a modified MLMC—control variate estimator and validate our results in various numerical examples.
@article{merkle2022multilevel,
abstract = {General elliptic equations with spatially discontinuous diffusion coefficients may be used as a simplified model for subsurface flow in heterogeneous or fractured porous media. In such a model, data sparsity and measurement errors are often taken into account by a randomization of the diffusion coefficient of the elliptic equation which reveals the necessity of the construction of flexible, spatially discontinuous random fields. Subordinated Gaussian random fields are random functions on higher dimensional parameter domains with discontinuous sample paths and great distributional flexibility. In the present work, we consider a random elliptic partial differential equation (PDE) where the discontinuous subordinated Gaussian random fields occur in the diffusion coefficient. Problem specific multilevel Monte Carlo (MLMC) Finite Element methods are constructed to approximate the mean of the solution to the random elliptic PDE. We prove a-priori convergence of a standard MLMC estimator and a modified MLMC—control variate estimator and validate our results in various numerical examples.},
added-at = {2024-03-26T11:56:35.000+0100},
affiliation = {Merkle, R (Corresponding Author), Univ Stuttgart, IANS SimTech, Stuttgart, Germany.
Merkle, Robin; Barth, Andrea, Univ Stuttgart, IANS SimTech, Stuttgart, Germany.},
author = {Merkle, Robin and Barth, Andrea},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2205a40f06db4c3f062bf9bf99b3447be/exc2075},
doi = {10.1007/s10543-022-00912-4},
interhash = {a76260528ac9ff4782b010f2c1418ced},
intrahash = {205a40f06db4c3f062bf9bf99b3447be},
issn = {{0006-3835} and {1572-9125}},
journal = {BIT Numerical Mathematics},
keywords = {PN5-4 PN5 EXC2075 selected},
language = {eng},
pages = {1279–1317},
publisher = {Springer},
pubstate = {prepublished},
research-areas = {Computer Science; Mathematics},
timestamp = {2024-03-26T11:56:35.000+0100},
title = {Multilevel Monte Carlo estimators for elliptic PDEs with Lévy-type diffusion coefficient},
unique-id = {WOS:000762880200002},
url = {https://doi.org/10.1007/s10543-022-00912-4},
volume = 62,
year = 2022
}
