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Model Reduction by Separation of Variables: A Comparison Between Hierarchical Model Reduction and Proper Generalized Decomposition

Simona Perotto 1 Michele Giuliano Carlino 2, 3 Francesco Ballarin 4
1 POLIMI - Politecnico di Milano [Milan]
2 IMB - Institut de Mathématiques de Bordeaux
3 MEMPHIS - Modeling Enablers for Multi-PHysics and InteractionS
IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest
4 SISSA MathLab [Trieste]
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.
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https://hal.inria.fr/hal-02927976
Contributor : Michele Giuliano Carlino <>
Submitted on : Wednesday, September 2, 2020 - 10:39:22 AM
Last modification on : Thursday, June 17, 2021 - 11:42:01 AM

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Simona Perotto, Michele Giuliano Carlino, Francesco Ballarin. Model Reduction by Separation of Variables: A Comparison Between Hierarchical Model Reduction and Proper Generalized Decomposition. Spencer J. Sherwin; David Moxey; Joaquim Peiró; Peter E. Vincent; Christoph Schwab. Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2018, LNCSE - Lecture Notes in Computational Science and Engineering (134), Springer, pp.61-77, 2020, ⟨10.1007/978-3-030-39647-3_4⟩. ⟨hal-02927976⟩

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