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

Journal
Proceedings of the National Academy of Sciences
Publisher
Proceedings of the National Academy of Sciences
Publication Date
Sep 22, 2009
Volume
106
Issue
38
Identifiers
DOI: 10.1073/pnas.0905547106
Keywords
Disciplines
  • Chemistry
  • Mathematics
License
Unknown

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.

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