Publications

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

Portone, Teresa P.; Niederhaus, John H.; Sanchez, Jason J.; Swiler, Laura P.

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.