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Numerical analysis of adaptive UQ algorithms for PDEs with random inputs
Bespalov, Alex
(Principal Investigator)
Mathematics
Overview
Fingerprint
Research output
(2)
Project Details
Short title
Numerical analysis of adaptive UQ algorithms for PDEs with random inputs
Status
Finished
Effective start/end date
20/06/17
→
31/07/21
Funding
Engineering & Physical Science Research Council
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Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
Adaptivity
Mathematics
100%
Partial differential equations
Engineering & Materials Science
98%
Finite Element Discretization
Mathematics
87%
Error Estimation
Mathematics
86%
Collocation
Mathematics
79%
Preconditioner
Mathematics
76%
Galerkin
Mathematics
76%
Truncation
Mathematics
76%
Research output
Research output per year
2021
2021
2022
2022
2
Article
Research output per year
Research output per year
Error estimation and adaptivity for stochastic collocation finite elements. Part I: single-level approximation
Bespalov, A.
,
Silvester, DJ.
&
Xu, F.
,
Oct 2022
,
In:
SIAM Journal on Scientific Computing.
44
,
5
,
p. A3393-A3412
20 p.
Research output
:
Contribution to journal
›
Article
›
peer-review
Open Access
File
Adaptivity
100%
Error Estimation
86%
Collocation
79%
Partial differential equations
79%
Error analysis
74%
8
Downloads (Pure)
Truncation preconditioners for Stochastic Galerkin finite element discretizations
Bespalov, A.
,
Loghin, D.
&
Youngnoi, R.
,
15 Mar 2021
, (E-pub ahead of print)
In:
SIAM Journal on Scientific Computing.
2021
,
p. S92-S116
Research output
:
Contribution to journal
›
Article
›
peer-review
Open Access
File
Finite Element Discretization
100%
Preconditioner
87%
Galerkin
87%
Truncation
87%
Galerkin Finite Element Method
72%
141
Downloads (Pure)