Model reduction by separation of variables: A comparison between hierarchical model reduction and proper generalized decomposition

Francesco Ballarin, Simona Perotto, Michele Giuliano Carlino

Risultato della ricerca: Contributo in libroContributo a convegno

1 Citazioni (Scopus)

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 to highlight pros and cons of the two procedures, both from a methodological and a numerical viewpoint.
Lingua originaleEnglish
Titolo della pubblicazione ospiteLecture Notes in Computational Science and Engineering
Pagine61-77
Numero di pagine17
Volume134
DOI
Stato di pubblicazionePubblicato - 2020
Evento12th International Conference on Spectral and High-Order Methods, ICOSAHOM 2018 - London
Durata: 9 lug 201813 lug 2018

Serie di pubblicazioni

NomeLECTURE NOTES IN COMPUTATIONAL SCIENCE AND ENGINEERING

Convegno

Convegno12th International Conference on Spectral and High-Order Methods, ICOSAHOM 2018
CittàLondon
Periodo9/7/1813/7/18

Keywords

  • hierarchical model reduction
  • proper generalized decomposition

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