Adaptive selection and validation of models of complex systems in the presence of uncertainty

Farrell-Maupin, Kathryn; Oden, J. T.

doi:10.1186/s40687-017-0104-2

Adaptive selection and validation of models of complex systems in the presence of uncertainty

Journal Article · Tue Aug 01 00:00:00 EDT 2017 · Research in the Mathematical Sciences

DOI:https://doi.org/10.1186/s40687-017-0104-2· OSTI ID:1356828

Farrell-Maupin, Kathryn ^[1]; ^[2]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Univ. of Texas, Austin, TX (United States)

This study describes versions of OPAL, the Occam-Plausibility Algorithm in which the use of Bayesian model plausibilities is replaced with information theoretic methods, such as the Akaike Information Criterion and the Bayes Information Criterion. Applications to complex systems of coarse-grained molecular models approximating atomistic models of polyethylene materials are described. All of these model selection methods take into account uncertainties in the model, the observational data, the model parameters, and the predicted quantities of interest. A comparison of the models chosen by Bayesian model selection criteria and those chosen by the information-theoretic criteria is given.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)

Grant/Contract Number:: AC04-94AL85000

OSTI ID:: 1356828

Report Number(s):: SAND--2017-2722J; PII: 104

Journal Information:: Research in the Mathematical Sciences, Journal Name: Research in the Mathematical Sciences Journal Issue: 1 Vol. 4; ISSN 2197-9847

Publisher:: SpringerOpenCopyright Statement

Country of Publication:: United States

Language:: English

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