Model Reduction by Separation of Variables: A Comparison Between Hierarchical Model Reduction and Proper Generalized Decomposition
CARLINO, Michele Giuliano
Institut de Mathématiques de Bordeaux [IMB]
Modeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]
Institut de Mathématiques de Bordeaux [IMB]
Modeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]
CARLINO, Michele Giuliano
Institut de Mathématiques de Bordeaux [IMB]
Modeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]
< Reduce
Institut de Mathématiques de Bordeaux [IMB]
Modeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]
Language
en
Chapitre d'ouvrage
This item was published in
Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2018, Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2018. 2020-08-12, vol. LNCSE - Lecture Notes in Computational Science and Engineering, n° 134, p. 61-77
Springer
English Abstract
Hierarchical Model reduction and Proper Generalized Decomposition both exploit separation of variables to perform a model reduction. After setting the basics, we exemplify these techniques on some standard elliptic problems ...Read more >
Hierarchical Model reduction and Proper Generalized Decomposition both exploit separation of variables to perform a model reduction. After setting the basics, we exemplify these techniques on some standard elliptic problems to highlight pros and cons of the two procedures, both from a methodological and a numerical viewpoint.Read less <
Origin
Hal imported