Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps.
Nonlinear independent component analysis is combined with diffusion-map data analysis techniques to detect good observables in high-dimensional dynamic data. These detections are achieved by integrating local principal component analysis of simulation bursts by using eigenvectors of a Markov matrix...
| Asıl Yazarlar: | , , , |
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| Materyal Türü: | Journal article |
| Dil: | English |
| Baskı/Yayın Bilgisi: |
2009
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| _version_ | 1826273803995447296 |
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| author | Singer, A Erban, R Kevrekidis, I Coifman, R |
| author_facet | Singer, A Erban, R Kevrekidis, I Coifman, R |
| author_sort | Singer, A |
| collection | OXFORD |
| description | Nonlinear independent component analysis is combined with diffusion-map data analysis techniques to detect good observables in high-dimensional dynamic data. These detections are achieved by integrating local principal component analysis of simulation bursts by using eigenvectors of a Markov matrix describing anisotropic diffusion. The widely applicable procedure, a crucial step in model reduction approaches, is illustrated on stochastic chemical reaction network simulations. |
| first_indexed | 2024-03-06T22:33:46Z |
| format | Journal article |
| id | oxford-uuid:5929f573-a19c-40ed-b7c5-90a201b29b76 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T22:33:46Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:5929f573-a19c-40ed-b7c5-90a201b29b762022-03-26T17:08:11ZDetecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:5929f573-a19c-40ed-b7c5-90a201b29b76EnglishSymplectic Elements at Oxford2009Singer, AErban, RKevrekidis, ICoifman, RNonlinear independent component analysis is combined with diffusion-map data analysis techniques to detect good observables in high-dimensional dynamic data. These detections are achieved by integrating local principal component analysis of simulation bursts by using eigenvectors of a Markov matrix describing anisotropic diffusion. The widely applicable procedure, a crucial step in model reduction approaches, is illustrated on stochastic chemical reaction network simulations. |
| spellingShingle | Singer, A Erban, R Kevrekidis, I Coifman, R Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps. |
| title | Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps. |
| title_full | Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps. |
| title_fullStr | Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps. |
| title_full_unstemmed | Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps. |
| title_short | Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps. |
| title_sort | detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps |
| work_keys_str_mv | AT singera detectingintrinsicslowvariablesinstochasticdynamicalsystemsbyanisotropicdiffusionmaps AT erbanr detectingintrinsicslowvariablesinstochasticdynamicalsystemsbyanisotropicdiffusionmaps AT kevrekidisi detectingintrinsicslowvariablesinstochasticdynamicalsystemsbyanisotropicdiffusionmaps AT coifmanr detectingintrinsicslowvariablesinstochasticdynamicalsystemsbyanisotropicdiffusionmaps |