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Comparison of Statistical and Deterministic Frameworks of Uncertainty Quantification

2016; Society for Industrial and Applied Mathematics; Volume: 4; Issue: 1 Linguagem: Inglês

10.1137/15m1019131

2166-2525

Michael Frenklach, Andrew Packard, Gonzalo García‐Donato, Rui Paulo, Jerome Sacks,

Advanced Multi-Objective Optimization Algorithms

Two different approaches to the prediction problem are compared employing a realistic example---combustion of natural gas---with 102 uncertain parameters and 76 quantities of interests. One approach, termed bound-to-bound data collaboration (abbreviated to B2B), deploys semidefinite programming algorithms where the initial bounds on unknowns are combined with initial bounds of experimental data to produce new uncertainty bounds for the unknowns that are consistent with the data and, finally, deterministic uncertainty bounds for prediction in new settings. The other approach is statistical and Bayesian, referred to as BCP (for Bayesian calibration and prediction). It places prior distributions on the unknown parameters and on the parameters of the measurement error distributions and produces posterior distributions for model parameters and posterior distributions for model predictions in new settings. The predictions from the two approaches are consistent; a very large degree of overlap exists between B2B bounds and the support of the BCP predictive distribution. Interpretation and comparison of the results is closely connected with assumptions made about the model and experimental data and how they are used in both settings. The principal conclusion is that use of both methods protects against possible violations of assumptions in the BCP approach and conservative specifications and predictions using B2B.