Manifold learning for parameter reduction
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 dimension...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | Journal of computational physics. - 1986. - 392(2019) vom: 01. Sept., Seite 419-431
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| 1. Verfasser: |
Holiday, Alexander
(VerfasserIn) |
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| Weitere Verfasser: |
Kooshkbaghi, Mahdi,
Bello-Rivas, Juan M,
Gear, C William,
Zagaris, Antonios,
Kevrekidis, Ioannis G |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2019
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| Zugriff auf das übergeordnete Werk: | Journal of computational physics
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| Schlagworte: | Journal Article
data driven perturbation theory
data mining
diffusion maps
model reduction
parameter sloppiness |
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