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Title: Fast variance reduction for steady-state simulation and sensitivity analysis of stochastic chemical systems using shadow function estimators

We address the problem of estimating steady-state quantities associated to systems of stochastic chemical kinetics. In most cases of interest, these systems are analytically intractable, and one has to resort to computational methods to estimate stationary values of cost functions. In this work, we introduce a novel variance reduction algorithm for stochastic chemical kinetics, inspired by related methods in queueing theory, in particular the use of shadow functions. Using two numerical examples, we demonstrate the efficiency of the method for the calculation of steady-state parametric sensitivities and evaluate its performance in comparison to other estimation methods.
Authors:
;  [1] ;  [2]
  1. Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel (Switzerland)
  2. Automatic Control Lab, ETH Zurich, Physikstrasse 3, 8092 Zurich (Switzerland)
Publication Date:
OSTI Identifier:
22308985
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Chemical Physics; Journal Volume: 141; Journal Issue: 2; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; ALGORITHMS; COMPARATIVE EVALUATIONS; EFFICIENCY; KINETICS; REDUCTION; SENSITIVITY ANALYSIS; SIMULATION; STEADY-STATE CONDITIONS; STOCHASTIC PROCESSES