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  4. Error estimates for SUPG-stabilised Dynamical Low Rank Approximations
 
conference paper

Error estimates for SUPG-stabilised Dynamical Low Rank Approximations

Nobile, Fabio  
•
Trigo Trindade, Thomas Simon Spencer  
Sequeira, Adélia
•
Silvestre, Ana
Show more
April 28, 2025
Numerical Mathematics and Advanced Applications ENUMATH 2023
European Conference on Numerical Mathematics and Advanced Applications

We perform an error analysis of a fully discretised Streamline Upwind Petrov Galerkin Dynamical Low Rank (SUPG-DLR) method for random time-dependent advection-dominated problems. The time integration scheme has a splitting-like nature, allowing for potentially efficient computations of the factors characterising the discretised random field. The method allows to efficiently compute a low-rank approximation of the true solution, while naturally "inbuilding" the SUPG stabilisation. Standard error rates in the L2 and SUPG-norms are recovered. Numerical experiments validate the predicted rates.

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Type
conference paper
DOI
10.1007/978-3-031-86169-7_45
ArXiv ID

2402.03586

Author(s)
Nobile, Fabio  

EPFL

Trigo Trindade, Thomas Simon Spencer  

EPFL

Editors
Sequeira, Adélia
•
Silvestre, Ana
•
Valtchev, Svilen
•
Janela, João
Date Issued

2025-04-28

Publisher

Springer Nature Switzerland

Publisher place

Cham

Published in
Numerical Mathematics and Advanced Applications ENUMATH 2023
DOI of the book
10.1007/978-3-031-86169-7_45
ISBN of the book

978-3-031-86168-0

978-3-031-86169-7

Book part number

Volume 2

Series title/Series vol.

Lecture Notes in Computational Science and Engineering; 154

ISSN (of the series)

2197-7100

1439-7358

Start page

438

End page

447

Subjects

Dynamical Low Rank Approximations

•

SUPG stabilisation

•

Error estimates

•

Random PDEs

URL

Link to the conference paper

https://link.springer.com/chapter/10.1007/978-3-031-86169-7_45
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CSQI  
Event nameEvent acronymEvent placeEvent date
European Conference on Numerical Mathematics and Advanced Applications

ENUMATH 2023

Lisbon, Portugal

2023-09-04 - 2023-09-08

FunderFunding(s)Grant NumberGrant URL

Swiss National Science Foundation

Dynamical low rank methods for uncertainty quantification and data assimilation

200518

https://data.snf.ch/grants/grant/200518
RelationRelated workURL/DOI

IsSupplementedBy

https://zenodo.org/records/10853290

IsCitedBy

Petrov-Galerkin Dynamical Low Rank Approximation: SUPG stabilisation of advection-dominated problems

https://infoscience.epfl.ch/handle/20.500.14299/240406
Available on Infoscience
April 29, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/206700.2
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