Stochastic multiscale modeling has become a popular approach to quantify uncertainty in multiscale models. The combination of multiscale features and complex uncertainty in models leads to great challenge to simulate the models and explore the propagation of uncertainty. To treat the difficulty, it is desirable to construct an accurate reduced order computational model for the original full order multiscale model. In the talk, we present a few stochastic multiscale model reduction methods and investigate their applications for uncertainty quantification of subsurface flows in random porous media.

姜立建，湖南大学数学院教授. 在美国Texas A&M大学获得应用数学博士学位。曾在美国ExxonMobil URC，洛斯阿拉莫斯国家试验室工作。目前主要研究方向是多尺度数值方法，不确定性量化及其在多孔介质模型中的应用。