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...
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Détails bibliographiques
| Publié dans: | Journal of computational physics. - 1986. - 392(2019) vom: 01. Sept., Seite 419-431
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| Auteur principal: |
Holiday, Alexander
(Auteur) |
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| Autres auteurs: |
Kooshkbaghi, Mahdi,
Bello-Rivas, Juan M,
Gear, C William,
Zagaris, Antonios,
Kevrekidis, Ioannis G |
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| Format: | Article en ligne
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| Langue: | English |
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| Publié: |
2019
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| Accès à la collection: | Journal of computational physics
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| Sujets: | Journal Article
data driven perturbation theory
data mining
diffusion maps
model reduction
parameter sloppiness |
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