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Title: Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota

Abstract

This report introduces the concepts of Bayesian model selection, which provides a systematic means of calibrating and selecting an optimal model to represent a phenomenon. This has many potential applications, including for comparing constitutive models. The ideas described herein are applied to a model selection problem between different yield models for hardened steel under extreme loading conditions.

Authors:
 [1];  [2];  [2];  [2]
  1. Univ. of Texas, Austin, TX (United States). Institute for Computational Engineering and Sciences
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1423930
Report Number(s):
SAND-2018-1889
660829
DOE Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Portone, Teresa, Niederhaus, John Henry, Sanchez, Jason James, and Swiler, Laura Painton. Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota. United States: N. p., 2018. Web. doi:10.2172/1423930.
Portone, Teresa, Niederhaus, John Henry, Sanchez, Jason James, & Swiler, Laura Painton. Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota. United States. doi:10.2172/1423930.
Portone, Teresa, Niederhaus, John Henry, Sanchez, Jason James, and Swiler, Laura Painton. Sat . "Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota". United States. doi:10.2172/1423930. https://www.osti.gov/servlets/purl/1423930.
@article{osti_1423930,
title = {Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota},
author = {Portone, Teresa and Niederhaus, John Henry and Sanchez, Jason James and Swiler, Laura Painton},
abstractNote = {This report introduces the concepts of Bayesian model selection, which provides a systematic means of calibrating and selecting an optimal model to represent a phenomenon. This has many potential applications, including for comparing constitutive models. The ideas described herein are applied to a model selection problem between different yield models for hardened steel under extreme loading conditions.},
doi = {10.2172/1423930},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {2}
}

Technical Report:

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