Basic Framework and Main Methods of Uncertainty Quantification

被引:32
|
作者
Zhang, Juan [1 ]
Yin, Junping [2 ]
Wang, Ruili [2 ]
机构
[1] Beihang Univ, Inst Artificial Intelligence, Beijing, Peoples R China
[2] Inst Appl Phys & Computat Math, Beijing, Peoples R China
关键词
GREENS-FUNCTION METHOD; SENSITIVITY-ANALYSIS; POLYNOMIAL CHAOS; SURROGATE MODELS; DESIGN; OPTIMIZATION; PROPAGATION; SIMULATIONS; CALIBRATION; VALIDATION;
D O I
10.1155/2020/6068203
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in many fields and has been highly valued and strongly supported by academia and industry. This review firstly discusses the sources and the types of uncertainties and gives an overall discussion on the goal, practical significance, and basic framework of the research of UQ. Then, the core ideas and typical methods of several important UQ processes are introduced, including sensitivity analysis, uncertainty propagation, model calibration, Bayesian inference, experimental design, surrogate model, and model uncertainty analysis.
引用
收藏
页数:18
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