Model reduction by separation of variables: a comparison between Hierarchical Model reduction and Proper Generalized Decomposition
arXiv:1811.114869 citationsh-index: 28
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It provides a comparative analysis for researchers choosing between these two model reduction techniques for elliptic problems.
The paper compares Hierarchical Model reduction and Proper Generalized Decomposition for model reduction using separation of variables, highlighting their pros and cons on standard elliptic problems from methodological and numerical perspectives.
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.