Author
Cucuringu, M
Erban, R
Journal title
SIAM Journal on Scientific Computing
Last updated
2024-03-27T22:13:11.193+00:00
Abstract
A method for detecting intrinsic slow variables in high-dimensional
stochastic chemical reaction networks is developed and analyzed. It combines
anisotropic diffusion maps (ADM) with approximations based on the chemical
Langevin equation (CLE). The resulting approach, called ADM-CLE, has the
potential of being more efficient than the ADM method for a large class of
chemical reaction systems, because it replaces the computationally most
expensive step of ADM (running local short bursts of simulations) by using an
approximation based on the CLE. The ADM-CLE approach can be used to estimate
the stationary distribution of the detected slow variable, without any a-priori
knowledge of it. If the conditional distribution of the fast variables can be
obtained analytically, then the resulting ADM-CLE approach does not make any
use of Monte Carlo simulations to estimate the distributions of both slow and
fast variables.
Symplectic ID
517507
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Publication type
Journal Article
Publication date
22 Feb 2017
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