Bayesian methods for characterizing unknown parameters of material models

J.M. Emery; M.D. Grigoriu; R.V. Field Jr.

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Bayesian methods for characterizing unknown parameters of material models

机译：贝叶斯方法表征材料模型的未知参数

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A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). The Bayesian method is also employed to characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.

机译：贝叶斯框架被开发用于表征材料性质的概率模型的未知参数。在此框架中，未知参数被视为随机参数，并通过从先验信息获得的后验分布以及对可观察到并取决于未知参数的感兴趣量的测量来描述。提出的贝叶斯方法用于表征随机输运方程定义中电导率场的未知空间相关性，并通过蒙特卡罗模拟和随机降阶模型（SROM）求解该方程。贝叶斯方法还用于根据激光焊接的峰值力的测量来表征激光焊接的材料性能的未知参数。

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来源
《Applied Mathematical Modelling》 |2016年第14期|6395-6411|共17页
作者
J.M. Emery; M.D. Grigoriu; R.V. Field Jr.;
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作者单位

Sandia National Laboratories, P.O. Box 5800 MS 0346, Albuquerque, New Mexico, United States;

Cornell University, School of Civil & Environmental Engineering, 220 Hollister Hall, Ithaca, NY, United State;

Sandia National Laboratories, Albuquerque, NM, United States;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Stochastic reduced order models; Bayesian method; Uncertain material parameters;

机译：随机降阶模型;贝叶斯方法不确定的材料参数;

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Bayesian methods for characterizing unknown parameters of material models

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