Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota
- Univ. of Texas, Austin, TX (United States). Institute for Computational Engineering and Sciences
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
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.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1423930
- Report Number(s):
- SAND-2018-1889; 660829
- Country of Publication:
- United States
- Language:
- English
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