Bayesian statistics for model prediction and reliability sensitivity analysis
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ABSTRACT
Uncertainty widely exists in many engineering problems, such as measurement uncertainty, model uncertainty, parameter uncertainty, and manufacturing uncertainty. These uncertainties will affect the design and manufacture of engineering products. An important issue is how to properly deal with these uncertainties and get more reliable and robust engineering products. In this talk, I will discuss using Bayesian statistics to deal with uncertainty and help provide a better model prediction, especially when the physical model is not accurate (a low-fidelity model), combing experimental measurement data. In addition, I will also discuss using Bayesian statistics for reliability sensitivity analysis, i.e., measuring the effects of random parameters on the failure of structures, which will be helpful for failure control and reliability-based design. Some results will be shown with composite material coupon structures.
BIOGRAPHY
Sinan Xiao received his BEng and PhD degrees in Aerospace Engineering from Northwestern Polytechnical University, China, in 2013 and 2018. After that, he became a postdoctoral researcher at the University of Stuttgart, Germany, supported by the Sino-German (CSC-DAAD) Postdoc Scholarship Program in 2018. Later, he worked simultaneously in Forschungszentrum Jülich, Germany as a postdoctoral researcher. Now, he is a research associate at the University of Bath, UK since 2021. His research interests include aerospace structure reliability design, Bayesian uncertainty quantification, structural failure detection, sensitivity analysis of model output, Gaussian process for machine learning, and Markov chain Monte Carlo simulation.