主讲人简介：

涂学民，美国堪萨斯大学数学系副教授, 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。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，获计算数学硕士学位；2006年毕业于美国纽约大学柯朗所，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和劳伦斯伯克利国家实验室做博士后研究。

内容摘要：

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 onuncertain models supplemented by a stream of data with noise. For nonlinearproblems, 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 samplingtechnique based on importance sampling. 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.

讲座主持：胡良剑 教授

讲座语言：英语