Model Reduction by Separation of Variables: A Comparison Between Hierarchical Model Reduction and Proper Generalized Decomposition - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Book Sections Year : 2020

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

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.

Dates and versions

hal-02927976 , version 1 (02-09-2020)

Identifiers

Cite

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⟩
50 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More