Dr. Sifeng Bi
Alexander von Humboldt Research Fellow
Institute for Risk and Reliability
Leibniz Universität Hannover
Tuesday, June 26, 2018, 13:00, Room 116, Institute for Risk and Reliability, LUH
Abstract
Numerical models, widely used for the design, optimization, and assessment of products, are approximate representations of reality in that their predictions exhibit a level of disagreement from experimental measurements. In the background of uncertainty analysis, the main sources of this disagreement can be classified as: 1) parameter uncertainties due to imprecisely known model parameters; 2) model form bias due to unavoidable simplifications and idealizations during modelling; and 3) test variability due to hard-to-control random effects in the experiments. These first two sources are related to the model and can be mitigated through the well-known process of model updating, which infers the likely parameter values and model bias that improve the agreement between predictions and measurements. Another relative process is sensitivity analysis, referring to the study of how uncertainty in the model prediction can be apportioned to different sources of uncertainty in the model parameter. Uncertainty quantification (UQ) metrics are consequently significant to provide a uniform, explicit, and quantitative description of the uncertainty information in stochastic model updating and sensitivity analysis. The Bhattacharyya distance is a statistical distance between two random samples considering their probabilistic distributions. In this presentation, this statistical distance is introduced as a comprehensive UQ metric, compared with the classical Euclidian distance. A complete framework of stochastic model updating and sensitivity analysis is proposed. And the role of the Bhattacharyya distance within this framework will be discussed.
Biography
Dr. Sifeng Bi is currently an Alexander von Humboldt research fellow in the Institute for Risk and Reliability, Leibniz Universität Hannover, Germany. He got his doctoral degree on aircraft design from Beihang University (also known as the Beijing University of Aeronautics and Astronautics), in 2015, and subsequently joined the Femto-ST Institute, France, as a post-doctoral researcher of the French National Center for Scientific Research (CNRS). Then he was granted the Humboldt Research Fellowship for Postdoctoral Researchers, and joined the Institute for Risk and Reliability since 2017. His research topics are uncertainty quantification, stochastic model updating and validation, especially in the application of vibroacoustics and complex structural dynamics. In particular, he focuses on probabilistic techniques such as advanced Monte Carlo simulation, approximate Bayesian computation, and global sensitivity analysis. His research also includes experimental modal analysis, noise and vibration controlling, decoupling of vibroacoustics, and damping identification with the consideration of uncertainties.
