skip to main content


Title: Global sensitivity analysis in stochastic simulators of uncertain reaction networks

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. Here, 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 analysismore » method classically used for this type of systems.« less
 [1] ; ORCiD logo [2] ;  [3]
  1. King Abdullah Univ. of Science and Technology (KAUST), Thuwal (Saudi Arabia). CEMSE Division
  2. King Abdullah Univ. of Science and Technology (KAUST), Thuwal (Saudi Arabia). CEMSE Division; Univ. Paris-Saclay, Gif-sur-Yvette (France). LIMSI and CNRS
  3. King Abdullah Univ. of Science and Technology (KAUST), Thuwal (Saudi Arabia). CEMSE Division; Duke Univ., Durham, NC (United States). Dept. of Mechanical Engineering and Materials Science
Publication Date:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 145; Journal Issue: 24; Journal ID: ISSN 0021-9606
American Institute of Physics (AIP)
Research Org:
Duke Univ., Durham, NC (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); King Abdullah Univ. of Science and Technology (KAUST), Thuwal (Saudi Arabia)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; Stochastic Process; Poisson's equation; Decomposition reactions; nonlinear dynamics; monte carlo methods; modeling; probability theory; real functions; mathematical physics; partial differential equations; reaction mechanisms
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1337433