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.
Perotto, S., Carlino, M. G., Ballarin, F., Model reduction by separation of variables: A comparison between hierarchical model reduction and proper generalized decomposition, Paper, in Lecture Notes in Computational Science and Engineering, (London, 09-13 July 2018), Springer, Cham 2020:<<LECTURE NOTES IN COMPUTATIONAL SCIENCE AND ENGINEERING>>,134 61-77. 10.1007/978-3-030-39647-3_4 [http://hdl.handle.net/10807/174183]
Model reduction by separation of variables: A comparison between hierarchical model reduction and proper generalized decomposition
Ballarin, Francesco
2020
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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