Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota | Zendy

Teresa Portone | Zendy; John Niederhaus | Zendy; Jason Sanchez | Zendy; Laura Swiler | Zendy

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

Author(s) -

Teresa Portone,

John Niederhaus,

Jason Sanchez,

Laura Swiler

Publication year - 2018

Publication title -

osti oai (u.s. department of energy office of scientific and technical information)

Language(s) - English

Resource type - Reports

DOI - 10.2172/1423930

Subject(s) - selection (genetic algorithm) , model selection , bayesian probability , yield (engineering) , bayesian inference , computer science , econometrics , machine learning , mathematics , artificial intelligence , materials science , metallurgy

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.

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