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

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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Publication date:
2009-01-01
URN:
uuid:ef96fcdc-09c6-4cf4-8b13-21fde18f3e22
Local pid:
oai:eprints.maths.ox.ac.uk:1637

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