期刊
JOURNAL OF MECHANICAL DESIGN
卷 134, 期 10, 页码 -出版社
ASME
DOI: 10.1115/1.4007573
关键词
multiple responses; Gaussian process; model updating; calibration; identifiability; uncertainty quantification; Multiple response emulator
资金
- National Science Foundation [CMMI-1233403, CMMI-0928320, CMMI-0758557]
- U.S. Army Tank-Automotive Research Development and Engineering Center (TARDEC) [W911NF11D0001-0037]
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1233403] Funding Source: National Science Foundation
In physics-based engineering modeling, the two primary sources of model uncertainty, which account for the differences between computer models and physical experiments, are parameter uncertainty and model discrepancy. Distinguishing the effects of the two sources of uncertainty can be challenging. For situations in which identifiability cannot be achieved using only a single response, we propose to improve identifiability by using multiple responses that share a mutual dependence on a common set of calibration parameters. To that end, we extend the single response modular Bayesian approach for calculating posterior distributions of the calibration parameters and the discrepancy function to multiple responses. Using an engineering example, we demonstrate that including multiple responses can improve identifiability (as measured by posterior standard deviations) by an amount that ranges from minimal to substantial, depending on the characteristics of the specific responses that are combined. [DOI: 10.1115/1.4007573]
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据