Representing Model Discrepancy in Bound-to-Bound Data Collaboration

We extend the existing methodology in bound-to-bound data collaboration (B2BDC), an optimizationbased 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 the feasible set is constructed according to a collection of modified model-data constraints. Formulas for making predictions are also modified to include the model discrepancy function. Prior information about the model discrepancy can be added to the framework as additional constraints. Dataset consistency, a central feature of B2BDC, is generalized based on the extended framework.

Automated summary

This study extends the methodology of bound-to-bound data collaboration in an optimization-based deterministic uncertainty quantification framework to explicitly account for model discrepancy. The discrepancy is represented as a linear combination of finite basis functions and modifications are made to prediction formulas. Prior information about model discrepancy can also be included as additional constraints to enhance framework accuracy. Dataset consistency, a central feature of B2BDC, is generalized based on the extended framework.