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数学与信息科学学院学术报告
20150701、20150702)
题目一:Inexact Balancing Domain Decomposition by Constraints Algorithms
 间:2015年7月1日(星期三)下午14:00
 
题目二:Implicit sampling for data assimilation
 间:2015年7月2日(星期四)上午9:00
 
报告人:涂学民(美国堪萨斯大学终身教授、博士生导师)
 点:先楼数信学院多媒体教室4-3425
 
 
摘要:
报告一:
Balancing domain decomposition by constraints (BDDC) algorithms are 
non-overlapping domain decomposition methods for solutions of large sparse linear algebraic systems arising from the discretization of boundary value problems. They are suitable for parallel computation. The coarse problem matrix of BDDC algorithms is generated and factored by a direct solver at the beginning of the computation. It will become a bottleneck when  the computer systems with a large number of processors are used. In this talk, an inexact coarse solver for BDDC algorithms is introduced and analyzed. This solver helps remove the bottleneck. At the same time, a good convergence rate is maintained. 
We will also talk about the extensions of these inexact BDDC algorithms to  problems arising from the mixed finite element and mortar finite element discretizations. 
 
报告二:
Applications of filtering and data assimilation arise in engineering, geosciences, weather forecasting, and many other areas where one has to make estimations or predictions based on uncertain models supplemented by a stream of data with noise. For nonlinear problems, filtering can be very expensive since the number of the particles required can grow catastrophically. We will present a particle-based nonlinear filtering scheme. This algorithm is based on implicit sampling, a new sampling technique related to chainless Monte Carlo method. This sampling strategy generates a particle (sample) beam which is focused towards on the high probability region of the target pdf and the focusing makes the number of particles required manageable even if the state dimension is large. Several examples will be given.
 
报告人简介:
涂学民, 1997年毕业于北京师范大学数学系,获理学学士学位; 2002年毕业于美国伍斯特工学院 (Worcester Polytechnic Institute),获计算数学硕士学位; 2006年毕业于美国纽约大学库朗所 (Courant Institute, New York University),获数学博士学位。2006年7月至2010年8月在美国加州大学伯克利分校 (University of California Berkeley)和劳伦斯伯克利国家实验室(Lawrence Berkeley National Laboratory) 做博士后研究。现任美国堪萨斯大学 (University of Kansas) 数学系副教授 (终身教授),  博士生导师。 研究领域:大型科学计算,区域分解算法,数据同化,非线性粒子滤波。主持美国国家自然科学基金2项。在Proceedings of the National Academy of Sciences, Journal of Computational Physics, SIAM Journal on Numerical Analysis, SIAM Journal on Scientific Computing 等杂志发表论文30余篇。