Date: July 20-24 (Monday to Friday)

Time: 8:00-11:00AM

Location: Tencent Meeting (腾讯会议)
Code: 976 599 2657
PWD: 202007 

Instructor: Xuemin Tu University of Kansas

Course Introduction: Data assimilation is an efficient way to combine computational models and observation data to obtain more accurate estimate for complicated systems. It has been widely used in science, engineer, and finance applications. This course will introduce mathematical theoretical background for data assimilation, difference algorithms and some applications. The main topics in this course includes:

1. Introduction to Data assimilation

2. Introduction to Monte Carlo methods

3. Kalman filter/smoother

4. Nonlinear data assimilation methods include: extended Kalman filter; ensemble Kalman filter; variational methods

5. Particle filters

Brief Introduction of the instructor: Dr. Xuemin Tu is a professor at University of Kansas. Her research areas includes nonlinear data assimilations especially for nonlinear non-Gaussian complicated systems. She has been working on implicit sampling and implicit particle filters and published in top journals such as PNAS and Month Weather Review.