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Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps
Amit Singer
, Radek Erban
, Ioannis G. Kevrekidis
, Ronald R. Coifman
Mathematics
Center for Statistics & Machine Learning
Chemical & Biological Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
128
Scopus citations
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Dive into the research topics of 'Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps'. Together they form a unique fingerprint.
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Engineering
Anisotropic Diffusion
100%
Slow Variable
100%
Observables
50%
Model Reduction
50%
Eigenvector
50%
Principal Components
50%
Component Analysis
50%
Independent Component Analysis
50%
Reaction Network
50%
Earth and Planetary Sciences
Data Transmission
100%
Eigenvector
100%
Principal Component Analysis
100%
Dynamical System
100%
Biochemistry, Genetics and Molecular Biology
Facilitated Diffusion
100%
Principal Component Analysis
33%
Mathematics
Applicable Procedure
50%
Chemical Reaction Networks
50%
Keyphrases
Stochastic Chemical Reaction Networks
50%