Mini symposia/Special sessions

MS13 Probability Density Methods for Uncertainty Quantification and Propagation

Prof. Jianbing Chen: chenjb@tongji.edu.cn

Session Chairs:
Jianbing Chen, Professor, Tongji University, chenjb@tongji.edu.cn;
Dixiong Yang, Professor, Dalian University of Technology, yangdx@dlut.edu.cn;
Yi Zhang, Associate Professor, Tsinghua University, zhang-yi@tsinghua.edu.cn;
Zhenhao Zhang, Associate Professor, Changsha University of Science and Technology, zhangzhenhao@csust.edu.cn.

Abstract of the special session:
Uncertainty quantification and propagation are the basis of refined performance evaluation, reliability assessment and risk based decision making of engineering structures and systems. To this end, great efforts have been devoted in this area in the past decades. The researches on uncertainty quantification and propagation cover all aspects of the effects of uncertainty in the systems of concern, including theory and methods to describe quantitatively the origin, propagation, and interplay of different sources of uncertainty in the analysis and predictions of the behavior of complex engineering systems. Current main methods for uncertainty quantification and propagation include the moment methods, spectral methods, Monte Carlo methods, the probability density methods, and so on. Among them, increasing importance has been attached to the probability density methods for uncertainty quantification and propagation, including the analytical methods and numerical methods focusing on obtaining accurately or approximately the probability density function of the quantity/process of interest, such as the structural stochastic response, first-passage time, multiple-passage problems, etc., in civil, mechanical, ocean and aerospace engineering communities.
This special session serves to support interactions among mathematicians, statisticians, engineers, and scientists working in the interface of computation, analysis, statistics, and probability to promote the development and innovation of the probability density methods for uncertainty quantification and propagation.

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