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This content will become publicly available on August 1, 2018

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

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:
Report Number(s):
SAND-2017-2722J
Journal ID: ISSN 2197-9847; PII: 104
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
Research in the Mathematical Sciences
Additional Journal Information:
Journal Volume: 4; Journal Issue: 1; Journal ID: ISSN 2197-9847
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)
Country of Publication:
United States
Language:
English
OSTI Identifier:
1356828