# 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:

- Univ. of Texas, Austin, TX (United States). Institute for Computational Engineering and Sciences
- 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}

}

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