# Representing Model Discrepancy in Bound-to-Bound Data Collaboration

## TL;DR

This paper enhances the B2BDC framework by explicitly modeling and incorporating model discrepancy through linear basis functions, improving uncertainty quantification and dataset consistency in deterministic models.

## Contribution

It introduces a method to explicitly represent model discrepancy within B2BDC using basis functions, extending the framework's capability for uncertainty quantification.

## Key findings

- Incorporates model discrepancy into B2BDC framework.
- Provides formulas for modified predictions including discrepancy.
- Generalizes dataset consistency to account for discrepancy.

## Abstract

We extended 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 was represented as a linear combination of finite basis functions and the feasible set was constructed according to a collection of modified model-data constraints. Formulas for making predictions were 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, was generalized based on the extended framework.