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

Abstract

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.

Authors:
 [1]; ORCiD logo [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Univ. of Texas, Austin, TX (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1356828
Report Number(s):
SAND-2017-2722J
Journal ID: ISSN 2197-9847; PII: 104
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Research in the Mathematical Sciences
Additional Journal Information:
Journal Volume: 4; Journal Issue: 1; Journal ID: ISSN 2197-9847
Publisher:
SpringerOpen
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Farrell-Maupin, Kathryn, and Oden, J. T. Adaptive selection and validation of models of complex systems in the presence of uncertainty. United States: N. p., 2017. Web. doi:10.1186/s40687-017-0104-2.
Farrell-Maupin, Kathryn, & Oden, J. T. Adaptive selection and validation of models of complex systems in the presence of uncertainty. United States. doi:10.1186/s40687-017-0104-2.
Farrell-Maupin, Kathryn, and Oden, J. T. Tue . "Adaptive selection and validation of models of complex systems in the presence of uncertainty". United States. doi:10.1186/s40687-017-0104-2. https://www.osti.gov/servlets/purl/1356828.
@article{osti_1356828,
title = {Adaptive selection and validation of models of complex systems in the presence of uncertainty},
author = {Farrell-Maupin, Kathryn and Oden, J. T.},
abstractNote = {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.},
doi = {10.1186/s40687-017-0104-2},
journal = {Research in the Mathematical Sciences},
number = 1,
volume = 4,
place = {United States},
year = {Tue Aug 01 00:00:00 EDT 2017},
month = {Tue Aug 01 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
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