Stochastic collocation method via compressed sensing and its application to UQ

报告题目:Stochastic collocation method via compressed sensing and its application to UQ


时间:2017 年 6 月 22号 14:00

地点:知新楼 B 座 924 报告厅


The field of uncertainty quantification (UQ) has undergone tremendous growth in recent years. Many new algorithms have been developed and used successfully to understand uncertainties in the computational simulation of large and complex problems. Though significant progress has been made, the curse of dimensionality remains a long-standing and cross-cutting challenge. The stochastic collocation method via compressed sensing (CS-SCM) is one of efficient approaches to solve this problem. This talk is devoted to presenting some progress in CS-SCM with UQ computations. To enhance the applicability and flexibility, we particularly introduced the $l_1-l_2$ minimization methods for sparse approximations of functions . We provide theoretical analysis on the validity of the approach. Several numerical examples are given to confirm the theoretical results.


闫亮,东南大学数学学院副教授。2011年毕业于兰州大学数学与统计学院,同年进入东南大学数学学院任教至今。目前主要从事随机计算与不确定量化、统计反问题与贝叶斯推断以及不适定问题的理论与算法的研究。主持完成国家自然科学基金青年项目和江苏省自然科学基金青年项目各一项。已经在《SIAM J. Sci. Comput.》、《Inverse Problems》、《J. Comput. Phys》等国内外刊物上发表十多篇学术论文. 2017年入选江苏省高校“青蓝工程”优秀青年骨干教师培养对象,2015年和2017年两次获得‘东南大学“吾爱吾师” 十大我最喜爱的老师’称号。