Representing Model Discrepancy in Bound-to-Bound Data Collaboration

We extend the existing methodology in bound-to-bound data collaboration (B2BDC), an optimization-based deterministic uncertainty quantification (UQ) framework, to explicitly take into account model discrepancy. The discrepancy is represented as a linear combination of finite basis functions, and feasible set constructed according collection modified model-data constraints. Formulas for making predictions are also include function. Prior information about can be added framework additional Dataset consistency, central feature B2BDC, generalized based on extended framework.