Representing Model Discrepancy in Bound-to-Bound Data Collaboration

Bibliographic Details
Title: Representing Model Discrepancy in Bound-to-Bound Data Collaboration
Authors: Li, Wenyu, Hegde, Arun, Oreluk, James, Packard, Andrew, Frenklach, Michael
Publication Year: 2019
Collection: Physics (Other)
Subject Terms: Physics - Data Analysis, Statistics and Probability, 62P35, 68T37
More Details: 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.
Comment: 31 pages, 10 figures and 7 tables
Document Type: Working Paper
Access URL: http://arxiv.org/abs/1907.00886
Accession Number: edsarx.1907.00886
Database: arXiv
More Details
Description not available.