Abstract
Most “learning” in big data is driven by the data alone. Some people may believe that this is sufficient because of the sheer data size. If the physical world is involved, however, this approach is often insufficient. In this talk, the speaker will give a recent study to illustrate how physics and data are used jointly to learn about the “truth” of the physical world. It also serves as an example of engineering analytics, which in itself has many forms and meanings. In an attempt to understand the turbulence behavior of an injector, a new design methodology is needed that combines engineering physics, computer simulations and statistical modeling. There are two key challenges: the simulation of high-fidelity spatial-temporal flows (using the Navier-Stokes equations) is computationally expensive; and the analysis and modeling of this data requires physical insights and statistical tools. A surrogate model is presented for efficient flow prediction in injectors with varying geometries, devices commonly used in many engineering applications. The novelty lies in incorporating properties of the fluid flow as simplifying model assumptions, which allows quick emulation in practical turnaround times and also reveals interesting flow physics that can guide further investigations.
About the speaker
Prof Jeff Wu received his PhD in Statistics from the University of California at Berkeley in 1976. He was formerly the HC Carver Professor of Statistics and Professor of Industrial and Operations Engineering at the University of Michigan, the GM/NSERC Chair in Quality and Productivity at the University of Waterloo. He also taught in the Statistics Department at the University of Wisconsin. He is currently a Professor in Industrial and Systems Engineering at the Georgia Institute of Technology, where he holds the Coca-Cola Chair in Engineering Statistics.
Prof Wu’s work is widely cited in professional journals as well as in magazines, including a feature article about his work in Canadian Business and a special issue of Newsweek on quality. He has served on editorial boards for several prestigious statistical journals such as the Annals of Statistics, the Journal of American Statistical Association, Technometrics, and Statistica Sinica. He has published more than 130 research articles in peer-reviewed journals.
Prof Wu has been affiliated with a number of prestigious organizations, namely the US National Academy of Engineering, the Academia Sinica and the Chinese Academy of Sciences. He is a Fellow of the American Society for Quality, Institute of Mathematical Statistics and American Statistical Association. Prof Wu has won numerous awards, including Taiwan’s Pan Wenyuan Technology Award (2008), the Jack Youden Prize twice for the best review paper in Technometrics (1997 and 2004), the Brumbaugh Award for the single most important paper to quality control among the publications sponsored by the American Society for Quality Control (1992), the Wilcoxon Prize for the best paper in Technometrics (1990) and the Committee of Presidents of Statistical Societies (COPSS) Presidents Award (1987). He was the PC Mahalanobis Memorial Lecturer at the Indian Statistical Institutes with well-known research work and a listing as an “ISI (Institute for Scientific Information) Highly Cited Researcher”. In 2008, he was awarded an Honorary Doctor (honoris causa) of Mathematics by the University of Waterloo.
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