Symbolic techniques for domain decomposition methods

T. Cluzeau; V. Dolean Maini; F. Nataf; A. Quadrat

doi:10.1007/978-3-642-35275-1_3

Symbolic techniques for domain decomposition methods

T. Cluzeau, V. Dolean Maini, F. Nataf, A. Quadrat

Mathematics And Statistics

Research output: Chapter in Book/Report/Conference proceeding › Chapter

3 Citations (Scopus)

Abstract

Some algorithmic aspects of systems of PDEs based simulations can be better clarified by means of symbolic computation techniques. This is very important since numerical simulations heavily rely on solving systems of PDEs. For the large-scale problems we deal with in today’s standard applications, it is necessary to rely on iterative Krylov methods that are scalable (i.e., weakly dependent on the number of degrees on freedom and number of subdomains) and have limited memory requirements.

Original language	English
Title of host publication	Domain Decomposition Methods in Science and Engineering XX
Editors	Randolph Bank, Michael Holst, Olof Widlund, Jinchao Xu
Place of Publication	Berlin
Publisher	Springer-Verlag
Pages	27-38
Number of pages	12
ISBN (Print)	9783642352744
DOIs	https://doi.org/10.1007/978-3-642-35275-1_3
Publication status	Published - 9 May 2013

Publication series

Name	Lecture Notes in Computational Science and Engineering
Publisher	Springer
Volume	91
ISSN (Print)	1439-7358

Keywords

computational mathematics
partial differential equations

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10.1007/978-3-642-35275-1_3

Cite this

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Symbolic techniques for domain decomposition methods. / Cluzeau, T.; Dolean Maini, V.; Nataf, F. et al.
Domain Decomposition Methods in Science and Engineering XX. ed. / Randolph Bank; Michael Holst; Olof Widlund; Jinchao Xu. Berlin: Springer-Verlag, 2013. p. 27-38 (Lecture Notes in Computational Science and Engineering; Vol. 91).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Symbolic techniques for domain decomposition methods

AU - Cluzeau, T.

AU - Dolean Maini, V.

AU - Nataf, F.

AU - Quadrat, A.

PY - 2013/5/9

Y1 - 2013/5/9

N2 - Some algorithmic aspects of systems of PDEs based simulations can be better clarified by means of symbolic computation techniques. This is very important since numerical simulations heavily rely on solving systems of PDEs. For the large-scale problems we deal with in today’s standard applications, it is necessary to rely on iterative Krylov methods that are scalable (i.e., weakly dependent on the number of degrees on freedom and number of subdomains) and have limited memory requirements.

AB - Some algorithmic aspects of systems of PDEs based simulations can be better clarified by means of symbolic computation techniques. This is very important since numerical simulations heavily rely on solving systems of PDEs. For the large-scale problems we deal with in today’s standard applications, it is necessary to rely on iterative Krylov methods that are scalable (i.e., weakly dependent on the number of degrees on freedom and number of subdomains) and have limited memory requirements.

KW - computational mathematics

KW - partial differential equations

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DO - 10.1007/978-3-642-35275-1_3

M3 - Chapter

SN - 9783642352744

T3 - Lecture Notes in Computational Science and Engineering

SP - 27

EP - 38

BT - Domain Decomposition Methods in Science and Engineering XX

A2 - Bank, Randolph

A2 - Holst, Michael

A2 - Widlund, Olof

A2 - Xu, Jinchao

PB - Springer-Verlag

CY - Berlin

ER -

Symbolic techniques for domain decomposition methods

Abstract

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Keywords

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