title: Sensitivity analysis of uncertainty in model prediction.
abstract: Data Collaboration is a framework designed to make inferences from experimental observations in the context of an underlying model. In the prior studies, the methodology was applied to prediction on chemical kinetics models, consistency of a reaction system, and discrimination among competing reaction models. The present work advances Data Collaboration by developing sensitivity analysis of uncertainty in model prediction with respect to uncertainty in experimental observations and model parameters. Evaluation of sensitivity coefficients is performed alongside the solution of the general optimization ansatz of Data Collaboration. The obtained sensitivity coefficients allow one to determine which experiment/parameter uncertainty contributes the most to the uncertainty in model prediction, rank such effects, consider new or even hypothetical experiments to perform, and combine the uncertainty analysis with the cost of uncertainty reduction, thereby providing guidance in selecting an experimental/theoretical strategy for community action.
authors.affiliation: Department of Mechanical Engineering, University of California, Berkeley, California 94720-1740, USA. trussi@berkeley.edu
authors.forename: Trent
authors.initials: T
authors.lastname: Russi
authors.forename: Andrew
authors.initials: A
authors.lastname: Packard
authors.forename: Ryan
authors.initials: R
authors.lastname: Feeley
authors.forename: Michael
authors.initials: M
authors.lastname: Frenklach