报告题目: | UQ Algorithms for Extreme-scale Systems | |
报 告 人: | Professor Dongbin Xiu | |
The University of Utah, USA | ||
报告时间: | 2014.4.26 上午 9:00-10:00 | |
报告地点: | 九龙湖数学系第一报告厅 | |
相关介绍: | 摘要: The field of uncertainty quantification (UQ) has received an increasing amount of attention recently. Extensive research efforts have been devoted to it and many novel numerical techniques have been developed. These techniques aim to conduct stochastic simulations for very large-scale complex systems. Although remarkable progresses have been made, UQ simulations remains challenging due to their exceedingly high simulation cost for problems at extreme scales. In this talk I will discuss some of the recent developed UQ algorithms that are particularly suitable for extreme-scale simulations. These methods are (1) collocation-based, such that they can be directly applied to systems with legacy simulation codes; and (2) capacity-based, such that they deliver the (near) optimal simulation accuracy based on the available simulation capacity. In another word, these methods deliver the best UQ simulation results based on any given computational resource one can afford, which is often very limited at the extreme scales. 报告人简介: Xiu Dongbin教授2004年获美国Brown University 应用数学博士学位,2005年加入普渡大学数学系,2009年任普渡大学数学系副教授,2012年任普渡大学数学系教授,2013年至今任犹他大学数学系及犹他大学科学计算与图像研究所教授。 Xiu Dongbin教授自2001年以来,在SIAM J. Sci. Comput.,SIAM Multiscale Model. Simul.,J. Comput. Phy等SCI杂志上发表论文50余篇,SCI他引近5,000余次,单篇SCI最高他引1000多次,有9篇论文单篇SCI引用超过100次,SCI h指数为24. Xiu Dongbin教授任多个国际SCI学术杂志的副主编、编委等,包括International Journal for Uncertainty Quantification 副主编(Jan 2011 - 至今),SIAM Review (Jan 2012- 至今),SIAM Journal on Scientific Computing (Jan 2011 � 至今),Applied Numerical Mathematics (Jan 2008 �至今),International Journal of Numerical Analysis and Modeling (Jan 2009 � 至今)等多个杂志的编委. Xiu Dongbin教授多年来一直从事随机计算与不确定量化、多变量函数逼近理论、随机优化理论、数据同化等方向的研究,在不确定量化等多个领域作出了贡献。他系统地研究了正交混沌分解理论(gPC)在随机计算中的应用,将该理论推广至一般的随机空间,从而将随机问题量化成为定量问题,使之更适合于实际应用。他的研究已使gPC方法成为处理不确定量化最有力的工具,并使该方向成为目前十分活跃的研究领域,并出版了专著《Numerical Methods for Stochastic Computations: A Spectral Method Approach》。 |