A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition
Keyword(s):
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.
2011 ◽
Vol 32
(12)
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pp. 2895-2900
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2001 ◽
Vol 24
(3)
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pp. 305-320
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2021 ◽
Vol 2006
(1)
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pp. 012069
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2007 ◽
Vol 15
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pp. 398-409
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2013 ◽
Vol 19
(2)
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pp. 291-305
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