|Title||Uncertainty Quantification for Complex Multiscale Systems|
|Publication Type||Conference Presentation|
|Year of Publication||2016|
|Authors||K. Bhat S, Mebane DS, Mahapatra P, Storlie CB|
|Date Published||April 5-8, 2016|
Multiscale modeling efforts must adequately quantify the effect of both parameter uncertainty and model discrepancy across scale. Advancements in uncertainty quantification using Bayesian calibration are described; a dynamic discrepancy approach to upscale uncertainty, functional inputs and extrapolation uncertainty, and a large parameter space. For emulation and discrepancy modeling, a Bayesian Smoothing Spline ANOVA (BSS-ANOVA) approach is utilized. These approaches are applied here to applications in chemical kinetics and carbon capture technology, with wide ranging impact.