Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps
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...
Asıl Yazarlar: | Singer, A, Erban, R, Kevrekidis, I, Coifman, R |
---|---|
Materyal Türü: | Journal article |
Baskı/Yayın Bilgisi: |
PNAS
2009
|
Benzer Materyaller
-
Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps.
Yazar:: Singer, A, ve diğerleri
Baskı/Yayın Bilgisi: (2009) -
Variable-free exploration of stochastic models: a gene regulatory network example.
Yazar:: Erban, R, ve diğerleri
Baskı/Yayın Bilgisi: (2007) -
ADM-CLE approach for detecting slow variables in continuous time Markov chains and dynamic data
Yazar:: Cucuringu, M, ve diğerleri
Baskı/Yayın Bilgisi: (2017) -
ADM-CLE approach for detecting slow variables in continuous time Markov
chains and dynamic data
Yazar:: Cucuringu, M, ve diğerleri
Baskı/Yayın Bilgisi: (2015) -
A constrained approach to multiscale stochastic simulation of chemically reacting systems.
Yazar:: Cotter, S, ve diğerleri
Baskı/Yayın Bilgisi: (2011)