期刊
MECHANICS OF ADVANCED MATERIALS AND STRUCTURES
卷 22, 期 8, 页码 641-654出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/15376494.2013.828819
关键词
Bayesian inference; model calibration; uncertainty quantification; predictive maturity; viscoplastic self-consistent; material plasticity models
类别
资金
- DOE Office of Nuclear Energy's Nuclear Energy University Programs [00101999]
In experiment-based validation, uncertainties and systematic biases in model predictions are reduced by either increasing the amount of experimental evidence available for model calibration-thereby mitigating prediction uncertainty-or increasing the rigor in the definition of physics and/or engineering principles-thereby mitigating prediction bias. Hence, decision makers must regularly choose between either allocating resources for experimentation or further code development. The authors propose a decision-making framework to assist in resource allocation strictly from the perspective of predictive maturity and demonstrate the application of this framework on a nontrivial problem of predicting the plastic deformation of polycrystals.
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