A multidimensional data-driven sparse identification technique: the sparse proper generalized decomposition
type: Item
instance of: scholarly article
title: A multidimensional data-driven sparse identification technique: the sparse proper generalized decomposition
zbMATH Open document ID: 1407.93063
DOI: 10.1155/2018/5608286
author: Xianqiang Yang
published in: Complexity
publication date: 2019-02-19T00:00:00Z
review text: Summary: Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional settings. This well-known phenomenon, coined as the curse of dimensionality, is here overcome by means of the use of separate representations. We present a technique based on the same principles of the Proper Generalized Decomposition that enables the identification of complex laws in the low-data limit. We provide examples on the performance of the technique in up to ten dimensions.
Mathematics Subject Classification ID: 93A15
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93B35
zbMATH DE Number: 7024982
describes a project that uses: SINDy
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/5608286
OpenAlex ID: W2899062473
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Wikidata QID: Q129074191
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description: scientific article; zbMATH DE number 7024982