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Efficient white noise sampling and coupling for multilevel Monte Carlo with nonnested meshes

Abstract:

When solving stochastic partial differential equations (SPDEs) driven by additive spatial white noise, the efficient sampling of white noise realizations can be challenging. Here, we present a new sampling technique that can be used to efficiently compute white noise samples in a finite element method (FEM) and multilevel Monte Carlo (MLMC) setting. The key idea is to exploit the finite element matrix assembly procedure and factorize each local mass matrix independently, hence avoiding the fa...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1137/18M1175239

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Oxford college:
University College
Role:
Author
ORCID:
0000-0003-1669-9445
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Mathematical Institute
Role:
Author
More from this funder
Name:
Nordic Council of Ministers, NordForsk
Grant:
74756
More from this funder
Name:
Engineering and Physical Sciences Research Council
Grant:
EP/L015803/1
Publisher:
Society for Industrial and Applied Mathematics
Journal:
SIAM/ASA Journal on Uncertainty Quantification More from this journal
Volume:
6
Issue:
4
Pages:
1630–1655
Publication date:
2018-11-20
Acceptance date:
2018-09-09
DOI:
EISSN:
2166-2525
Language:
English
Keywords:
Pubs id:
pubs:865752
UUID:
uuid:09b3c132-87d5-4dee-8825-a2c0ad21ca73
Local pid:
pubs:865752
Source identifiers:
865752
Deposit date:
2018-09-19

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