报告人简介：

金石，上海交通大学自然科学学院院长、讲席教授。先后获北京大学学士学位，美国亚利桑那大学博士学位，历任美国纽约大学库朗数学研究所博士后，美国佐治亚理工学院助理教授、副教授，美国威斯康星大学（麦迪逊）正教授、数学系系主任、Vilas杰出成就教授，上海交通大学数学系系主任。曾获得冯康科学计算奖，国家自然科学基金杰出青年基金（海外），国际华人数学家大会晨兴数学银奖。他是美国数学会（AMS）首批会士，工业与应用数学学会(SIAM)会士，及2018年国际数学家大会邀请报告人。

报告摘要：

Hypocoercivity based analysis is apowerful tool for kinetic equations which allows one to understand theregularity and long-time behavior of both linear and nonlinear kineticequations, despite that kinetic operators are degenerately dissipative. Weextend such analysis to linear and nonlinear kinetic equations with random uncertaintiesin initial data or collisional kernels, which allows us to establishregularity, local sensitivity with respect to uncertain random parameters, andlong-time exponential decay of the solution toward the global equilibrium inthe random space, as well as spectral convergence and long-time error decay ofthe polynomial chaosbased stochastic Galerkin methods, a popular method usedfor uncertainty quantification.

报告主持：秦玉明

报告语言：英语