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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1016/j.jcp.2019.04.015
|2 doi
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|a pubmed24n0991.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Holiday, Alexander
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Manifold learning for parameter reduction
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|c 2019
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 11.10.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Large scale dynamical systems (e.g. many nonlinear coupled differential equations) can often be summarized in terms of only a few state variables (a few equations), a trait that reduces complexity and facilitates exploration of behavioral aspects of otherwise intractable models. High model dimensionality and complexity makes symbolic, pen-and-paper model reduction tedious and impractical, a difficulty addressed by recently developed frameworks that computerize reduction. Symbolic work has the benefit, however, of identifying both reduced state variables and parameter combinations that matter most (effective parameters, "inputs"); whereas current computational reduction schemes leave the parameter reduction aspect mostly unaddressed. As the interest in mapping out and optimizing complex input-output relations keeps growing, it becomes clear that combating the curse of dimensionality also requires efficient schemes for input space exploration and reduction. Here, we explore systematic, data-driven parameter reduction by means of effective parameter identification, starting from current nonlinear manifoldlearning techniques enabling state space reduction. Our approach aspires to extend the data-driven determination of effective state variables with the data-driven discovery of effective model parameters, and thus to accelerate the exploration of high-dimensional parameter spaces associated with complex models
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|a Journal Article
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|a data driven perturbation theory
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| 650 |
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4 |
|a data mining
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| 650 |
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|a diffusion maps
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| 650 |
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|a model reduction
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| 650 |
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|a parameter sloppiness
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| 700 |
1 |
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|a Kooshkbaghi, Mahdi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bello-Rivas, Juan M
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gear, C William
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zagaris, Antonios
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kevrekidis, Ioannis G
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of computational physics
|d 1986
|g 392(2019) vom: 01. Sept., Seite 419-431
|w (DE-627)NLM098188844
|x 0021-9991
|7 nnns
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| 773 |
1 |
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|g volume:392
|g year:2019
|g day:01
|g month:09
|g pages:419-431
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| 856 |
4 |
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|u http://dx.doi.org/10.1016/j.jcp.2019.04.015
|3 Volltext
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|d 392
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