MS53 Bayesian Inference and Uncertainty Quantification: New Methods and Applications

Prof. Yong Huang: huangyong@hit.edu.cn

Session Chairs:
Yong Huang, Professor, Harbin Institute of Technology, China, huangyong@hit.edu.cn
Stephen Wu, Associate Professor, The Institute of Statistical Mathematics, Japan, stewu@ism.ac.jp
James L. Beck, Professor, California Institute of Technology, USA, jimbeck@caltech.edu

Abstract of the special session:
This mini-symposium focuses on recent theoretical, computational and application advances in using Bayesian inference and uncertainty quantification for structural system identification and health monitoring. Applications in civil engineering, mechanical and aerospace engineering, as well as other related engineering disciplines are welcomed. Possible topics include but are not limited to:

1. Advanced stochastic simulation techniques for Bayesian inference, such as Markov chain Monte Carlo algorithms, Approximate Bayesian computation algorithms and Bayesian filtering algorithms
2. Advances in Bayesian methods for big data and machine learning
3. Bayesian modal identification and operational modal analysis
4. Bayesian methods for signal processing, damage detection, localization, quantification and prognosis
5. Bayesian experimental design, including optimal sensor and actuator location methods, and
6. Stochastic/probabilistic models for damage evolution, including crack propagation and fatigue.