Adaptive selection and validation of models of complex systems in the presence of uncertainty
This study describes versions of OPAL, the OccamPlausibility 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 coarsegrained 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 informationtheoretic criteria is given.
 Authors:

^{[1]};
^{[2]}
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Univ. of Texas, Austin, TX (United States)
 Publication Date:
 Report Number(s):
 SAND20172722J
Journal ID: ISSN 21979847; PII: 104
 Grant/Contract Number:
 AC0494AL85000
 Type:
 Accepted Manuscript
 Journal Name:
 Research in the Mathematical Sciences
 Additional Journal Information:
 Journal Volume: 4; Journal Issue: 1; Journal ID: ISSN 21979847
 Publisher:
 SpringerOpen
 Research Org:
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Sponsoring Org:
 USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC21)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING
 OSTI Identifier:
 1356828
FarrellMaupin, Kathryn, and Oden, J. T.. Adaptive selection and validation of models of complex systems in the presence of uncertainty. United States: N. p.,
Web. doi:10.1186/s4068701701042.
FarrellMaupin, Kathryn, & Oden, J. T.. Adaptive selection and validation of models of complex systems in the presence of uncertainty. United States. doi:10.1186/s4068701701042.
FarrellMaupin, Kathryn, and Oden, J. T.. 2017.
"Adaptive selection and validation of models of complex systems in the presence of uncertainty". United States.
doi:10.1186/s4068701701042. 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 = {FarrellMaupin, Kathryn and Oden, J. T.},
abstractNote = {This study describes versions of OPAL, the OccamPlausibility 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 coarsegrained 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 informationtheoretic criteria is given.},
doi = {10.1186/s4068701701042},
journal = {Research in the Mathematical Sciences},
number = 1,
volume = 4,
place = {United States},
year = {2017},
month = {8}
}