报告人：谈晓思博士

Harold Vance Department of Petroleum Engineering,

Texas A&M University, USA

报告题目：Multilevel Uncertainty Quantification Techniques Using Multiscale Methods

报告时间：2016年12月22日14:30

报告地点：海韵数理楼661

联系人：陈黄鑫副教授

内容摘要：

In this talk, we discuss a multilevel framework for the uncertainty quantification in inverse problems. It is based on the Generalized Multiscale Finite Element Method (GMsFEM) and Multilevel Monte Carlo (MLMC) methods. The former provides a hierarchy of approximations at different resolutions, whereas the latter gives an efficient way to estimate quantities of interest using samples on different levels. By suitably choosing the number of samples at different levels, one can use less of expensive forward simulations on the fine grid, while more of inexpensive forward simulations on the coarse grid in Monte Carlo simulations. Further, a Multilevel Markov Chain Monte Carlo (MLMCMC) method is discussed, which sequentially screens the proposal with different levels of approximations and reduces the number of evaluations required on the fine grid, while combining the samples at different levels to arrive at an accurate estimate. The framework seamlessly integrates the multiscale feature of the GMsFEM with the multilevel feature of the MLMC methods. Numerical results will be presented.

报告人简介：

Xiaosi Tan is a post-doctoral-degree research associate in the Harold Vance Department of Petroleum Engineering at Texas A&M University. Her research interests include shale gas simulation, multiscale methods for differential equations, uncertainty quantification, multilevel Monte Carlo and Bayesian inversion, and model reduction of large-scale systems with an emphasis on petroleum-engineering problems. Tan holds a PhD degree in applied mathematics from Texas A&M University and a BS degree in applied mathematics from Beijing University of Technology in China.

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