报告简介🎂：

In this minicourse, we introduce the uncertainty quantification for complex nonlinear system. Since the perfect model, i.e., true nature, is typically unknown, variety kinds of imperfect models are proposed as the low order reduced model. We will study the uncertainty quantification, model error, model sensitivity, prediction skill and information-theoretic optimization in the imperfect models. Most of the materials in these applied math lectures are based on the recent work of Professor Andrew J. Majda and his collaborators.

课程一： Introduction to Uncertainty Quantification, Fluctuation-dissipation Theory and Empirical Information theory (2013年12月23日 上午9:30-11:00）

课程二： Stochastic Toolkit for Uncertainty Quantification in Complex Nonlinear Systems (2013年12月24日 上午9:30-11:00）

课程三： Uncertainty Quantification in Simple Models with Hidden Instabilities (2013年12月25日 上午9:30-11:00）

课程四： Uncertainty Quantification in Turbulent Spatially Extended Systems (2013年12月26日 上午9:30-11:00）

课程五： Ensemble Prediction for Complex Nonlinear System with Model Error (2013年12月28日 上午9:30-11:00）