Global sensitivity analysis in stochastic simulators of uncertain reaction networks
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2016-12-26
Print Publication Date2016-12-28
Permanent link to this recordhttp://hdl.handle.net/10754/622678
MetadataShow full item record
AbstractStochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol’s decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.
CitationNavarro Jimenez M, Le Maître OP, Knio OM (2016) Global sensitivity analysis in stochastic simulators of uncertain reaction networks. The Journal of Chemical Physics 145: 244106. Available: http://dx.doi.org/10.1063/1.4971797.
SponsorsThis work was supported in part by the SRI Center for Uncertainty Quantification in Computational Science and Engineering at King Abdullah University of Science and Technology, and by the US Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research, under Award No. DE-SC0008789.
JournalThe Journal of Chemical Physics