Embedded Model Error Representation for Bayesian Model Calibration

Journal Article · Tue Feb 05 04:00:00 EST 2019 · arXiv.org Repository

OSTI ID:1529284

Huan, Xun ^[1]; Sargsyan, Khachik ^[1]; Najm, Habib N. ^[1]

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1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)

Model error estimation remains one of the key challenges in uncertainty quantification and predictive science. For computational models of complex physical systems, model error, also known as structural error or model inadequacy, is often the largest contributor to the overall predictive uncertainty. This work builds on a recently developed framework of embedded, internal model correction, in order to represent and quantify structural errors, together with model parameters,within a Bayesian inference context. We focus specifically on a Polynomial Chaos representation with additive modification of existing model parameters, enabling a non-intrusive procedure for efficient approximate likelihood construction, model error estimation, and disambiguation of model and data errors’ contributions to predictive uncertainty. The framework is demonstrated on several synthetic examples, as well as on a chemical ignition problem.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: DARPA; USDOE
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1529284
• Report Number(s): SAND--2018-0669J; 660113
• Journal Information: arXiv.org Repository, Journal Name: arXiv.org Repository Vol. 2019; ISSN 9999-0017
• Publisher: Cornell University
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
97 MATHEMATICS AND COMPUTING
Approximate Bayesian computation
Bayesian inference
Model error
Polynomial chaos
Structural error
Uncertainty quantification