Sequential data assimilation for uncertainty quantification and parameters estimations in stochastic systems

Publisher:闻天明Release Time:2021-06-06Number of visits:10

Speaker:     Prof. Peng Wang

Time:          Jun.7.2021 10:00-11:00

Location:    SIST 1C101

Host:            Prof. Qifeng Liao

Abstract:

Sequential data assimilation methods such as Kalman filter and alike have become essential tools in predicting uncertain system states prone to model error and observation noise. In this talk, we will introduce a few such tools that we have developed over the years, such as multi-model sequential data assimilations, integrated quantification of parametric and model uncertainty, system parameters estimations for lithium battery temperature.    

Bio:

王鹏博士,现任北京航空航天大学集成电路科学与工程学院教授、博导,主要研究方向为不确定性量化理论方法与工程应用。博士毕业于美国加州大学圣迭戈分校,后就职于美国能源部太平洋西北国家实验室研究员。2014年入选国家级海外高层次青年人才项目,在北京航空航天大学数学科学学院、国际交叉科学研究院担任教授、博导。先后主持、参与4项科技部国家重点研发计划和国家基金委基金委面上项目。他的团队不仅在数据同化、不确定性量化等基础理论上提出了一系列全新的共性底层算法,也在环境、能源、多孔介质、材料基因、药物、电子等领域取得了极具应用前景的示范验证.

SIST Seminar 202112