The Mathematical Institute, University of Oxford, Eprints Archive

Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps

Singer, A and Erban, R. and Kevrekidis, I. G. and Coifman, R (2009) Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps. Proceedings of the National Academy of Sciences, 106 (38). pp. 16090-16095. ISSN 1091-6490

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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.

Item Type:Article
Uncontrolled Keywords:slow manifold dimensionality reduction chemical reactions
Subjects:A - C > Biology and other natural sciences
Research Groups:Centre for Mathematical Biology
ID Code:1637
Deposited By: Sara Jolliffe
Deposited On:15 Dec 2012 13:52
Last Modified:29 May 2015 19:19

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