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Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps.
- Source :
-
Proceedings of the National Academy of Sciences of the United States of America . 9/22/2009, Vol. 106 Issue 38, p16090-16095. 6p. - Publication Year :
- 2009
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00278424
- Volume :
- 106
- Issue :
- 38
- Database :
- Academic Search Index
- Journal :
- Proceedings of the National Academy of Sciences of the United States of America
- Publication Type :
- Academic Journal
- Accession number :
- 44749833
- Full Text :
- https://doi.org/10.1073/pnas.0905547106