Adaptive selection and validation of models of complex systems in the presence of uncertainty
Journal Article
·
· Research in the Mathematical Sciences
- 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.
- 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
Bayesian calibration of force-fields from experimental data: TIP4P water
|
journal | October 2018 |
Bayesian Calibration of Force-fields from Experimental Data: TIP4P Water
|
text | January 2018 |
Embedded discrepancy operators in reduced models of interacting species | preprint | January 2019 |
Similar Records
A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems
On Model Selection Criteria in Multimodel Analysis
Bayesian modeling of source confusion in LISA data
Journal Article
·
2015
· Journal of Computational Physics
·
OSTI ID:22465641
On Model Selection Criteria in Multimodel Analysis
Journal Article
·
2008
· Water Resources Research, 44(3):Art. no. W03428
·
OSTI ID:927977
Bayesian modeling of source confusion in LISA data
Journal Article
·
2005
· Physical Review. D, Particles Fields
·
OSTI ID:20711097
+5 more