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Université
Paris 13
MACS 2 - Advanced
Numerical Analysis - 2
Professors : Caroline Japhet and Michel Kern
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In scientific computing, complex
physical phenomena can be formulated as systems of partial
differential equations (PDE), for example the airflow
around an aircraft, the evolution
of a glacier or the temperature in
your room. Most of these equations however can not be solved
exactly by analytical tools. Their solution has to be
approximated using numerical methods, involving the solving of
linear systems of large size, that cannot be solved on one
computer.
Krylov methods (e.g. GMRES), Preconditioning techniques, and
Domain Decomposition Methods (DDM) are iterative methods commonly used for
solving these large problems (with sparse matrices).
The principle of DDM is to transform
the problem (the PDE or the linear system) into a series
of decoupled subproblems, of smaller size, which can be
solved in parallel on several processors. The saving of time is then considerable. These methods are also widely used to
couple different models (e.g. ocean and atmosphere models
to predict cyclones).
The goal of this course is to study :
- Krylov methods (Conjugate Gradient, GMRES) and
Preconditioners
- a class
of DDM : the Schwarz and Optimized Schwarz methods.
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Cours
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cours
References
- M. Kern. Cours d’Analyse Numérique
Avancée 1, MACS 2, 2020-2021.
- F. Cuvelier. Cours d’Analyse
Numérique Elémentaire, MACS 1, 2020.
- M.J. Gander and L. Halpern.
Méthodes de décomposition de domaines - Notions de base. In
Encyclopédie
des techniques de l’ingénieur, méthodes numériques. AF137, 2012.
- P.-L. Lions. On the Schwarz
alternating method. I. In R. Glowinski, G. H. Golub, G. A.
Meurant, and J. Pé-
riaux, editors, First International Symposium on Domain
Decomposition Methods for Partial Differential
Equations, pages 1–42. Philadelphia, PA, SIAM, 1988.
- H. A. Schwarz. Über einen
Grenzübergang durch alternierendes Verfahren.
Vierteljahrsschrift der Natur-
forschenden Gesellschaft in Zürich, 15 :272–286, May 1870.
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