学术报告|
当前位置:首页 > 科研 > 学术报告
发表时间:2020-01-06 阅读次数:376次
报告题目: A Nonlinear Conditional Gaussian Framework for Extreme Events Prediction, State Estimation and Uncertainty Quantification in Complex Dynamical System
报 告 人:Nan Chen
报告人所在单位:University of Wisconsin-Madison
报告日期:2020-01-06 星期一
报告时间:9:00-10:00
报告地点:光华东主楼1801
  
报告摘要:
    A conditional Gaussian framework for uncertainty quantification, data assimilation and prediction of nonlinear turbulent dynamical systems will be introduced in this talk. Despite the conditional Gaussianity, the dynamics remain highly nonlinear and are able to capture strongly non-Gaussian features such as intermittency and extreme events. The conditional Gaussian structure allows efficient and analytically solvable conditional statistics that facilitates the real-time data assimilation and prediction. The talk will include three applications of such conditional Gaussian framework. In the first part, a physics-constrained nonlinear stochastic model is developed, and is applied to predicting the Madden-Julian oscillation indices with strongly non-Gaussian intermittent features. The second part regards the state estimation and data assimilation of multiscale and turbulent ocean flows using noisy Lagrangian tracers. Rigorous analysis shows that an exponential increase in the number of tracers is required for reducing the uncertainty by a fixed amount. This indicates a practical information barrier. In the last part of the talk, an efficient statistically accurate algorithm is developed that is able to solve a rich class of high dimensional Fokker-Planck equation with strong non-Gaussian features and beat the curse of dimensions. The method is useful for ensemble prediction of complex nonlinear dynamical systems.
 
  
本年度学院报告总序号:4

Copyright © |2012 复旦大学数学科学学院版权所有 沪ICP备042465  

电话:+86(21)65642341 传真:+86(21)65646073