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.

Model reduction by separation of variables: A comparison between hierarchical model reduction and proper generalized decomposition / Perotto, S.; Carlino, M. G.; Ballarin, F.. - 134:(2020), pp. 61-77. [10.1007/978-3-030-39647-3_4]

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

Perotto, S.
;
Ballarin, F.
2020-01-01

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.
2020
134
Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2018
61
77
10.1007/978-3-030-39647-3_4
https://arxiv.org/abs/1811.11486
Perotto, S.; Carlino, M. G.; Ballarin, F.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/118109
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