A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition
Financiación H2020 / H2020 Funds
Resumen: 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.
Idioma: Inglés
DOI: 10.1155/2018/5608286
Año: 2018
Publicado en: Complexity 18, 5608286 (2018), [11 pp]
ISSN: 1076-2787

Factor impacto JCR: 2.591 (2018)
Categ. JCR: MATHEMATICS, INTERDISCIPLINARY APPLICATIONS rank: 21 / 105 = 0.2 (2018) - Q1 - T1
Categ. JCR: MULTIDISCIPLINARY SCIENCES rank: 24 / 69 = 0.348 (2018) - Q2 - T2

Factor impacto SCIMAGO: 0.535 - Multidisciplinary (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/T24-17R
Financiación: info:eu-repo/grantAgreement/EC/H2020/675919/EU/Empowered decision-making in simulation-based engineering: Advanced Model Reduction for real-time, inverse and optimization in industrial problems/AdMoRe
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2015-72365-EXP
Financiación: info:eu-repo/grantAgreement/ES/MINECO/DPI2017-85139-C2-1-R
Tipo y forma: Article (Published version)
Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)

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 Record created 2018-12-18, last modified 2020-05-13

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