Global sensitivity analysis in stochastic simulators of uncertain reaction networks

Handle URI:
http://hdl.handle.net/10754/622678
Title:
Global sensitivity analysis in stochastic simulators of uncertain reaction networks
Authors:
Navarro, María; Le Maitre, Olivier; Knio, Omar
Abstract:
Stochastic 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Navarro 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.
Publisher:
AIP Publishing
Journal:
The Journal of Chemical Physics
Issue Date:
26-Dec-2016
DOI:
10.1063/1.4971797
Type:
Article
ISSN:
0021-9606; 1089-7690
Sponsors:
This 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.
Additional Links:
http://aip.scitation.org/doi/10.1063/1.4971797
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorNavarro, Maríaen
dc.contributor.authorLe Maitre, Olivieren
dc.contributor.authorKnio, Omaren
dc.date.accessioned2017-01-11T12:20:30Z-
dc.date.available2017-01-11T12:20:30Z-
dc.date.issued2016-12-26en
dc.identifier.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.en
dc.identifier.issn0021-9606en
dc.identifier.issn1089-7690en
dc.identifier.doi10.1063/1.4971797en
dc.identifier.urihttp://hdl.handle.net/10754/622678-
dc.description.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.en
dc.description.sponsorshipThis 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.en
dc.publisherAIP Publishingen
dc.relation.urlhttp://aip.scitation.org/doi/10.1063/1.4971797en
dc.rightsThis article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. The following article appeared in Navarro Jimenez, M., Le Maître, O.P. and Knio, O.M., 2016. Global sensitivity analysis in stochastic simulators of uncertain reaction networks. The Journal of Chemical Physics, 145(24), p.244106. and may be found at http://aip.scitation.org/doi/10.1063/1.4971797.en
dc.titleGlobal sensitivity analysis in stochastic simulators of uncertain reaction networksen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalThe Journal of Chemical Physicsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionLIMSI, CNRS, Université Paris-Saclay, Paris, Franceen
dc.contributor.institutionDepartment of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina, 27708, United Statesen
kaust.authorNavarro, Maríaen
kaust.authorLe Maitre, Olivieren
kaust.authorKnio, Omaren
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