报告主题：An adaptive ANOVA stochastic Galerkin method for partial differential equations with random inputs
时间：2023年10月20日13:00-13:45
地点：上川路校区二教105/腾讯会议 会议号：984 1292 8754 密码：6666报告人：王官杰
报告内容简介：
It is known that standard stochastic Galerkin methods encounter challenges when solving partial differential equations with high dimensional random inputs, which are typically caused by the large number of stochastic basis functions required. It becomes crucial to properly choose effective basis functions, such that the dimension of the stochastic approximation space can be reduced. In this work, we focus on the stochastic Galerkin approximation associated with generalized polynomial chaos (gPC), and explore the gPC expansion based on the analysis of variance (ANOVA) decomposition. A concise form of the gPC expansion is presented for each component function of the ANOVA expansion, and an adaptive ANOVA procedure is proposed to construct the overall stochastic Galerkin system. Numerical results demonstrate the efficiency of our proposed adaptive ANOVA stochastic Galerkin method.
主讲人简介：王官杰，上海立信会计金融学院讲师。2015年6月毕业于浙江大学数学系，获理学博士学位，2015年7月至2018年3月在中国科学院微系统与信息技术研究所从事博士后研究工作，2018年4月至2020年2月在上海科技大学任助理研究员。他的主要研究方向为微分方程数值解及不确定性量化，目前已发表相关论文多。