讲座题目：Stabilized SAV ensemble algorithms for parameterized flow problems

主办单位：三峡数学研究中心/理学院

报告专家：杨欢欢博士（汕头大学）

报告时间：2022年6月29日（周三） 下午（14:30—17:30）

报告地点：腾讯会议（ID： 504 848 731）

专家简介：杨欢欢，汕头大学数学系副教授。2015年于美国埃默里大学毕业获得博士学位，之后两年在佛罗里达州立大学从事博士后工作。主要研究领域为偏微分方程数值解。学术论文发表在SIAM J. Numer. Anal., SIAM J. Sci. Comput., J. Comput. Phys., Inverse Probl., Comput. Methods Appl. Mech. Eng.等学术期刊。主持广东高校省级重大科研项目(青年创新人才类)一项、国家自然科学基金青年科学基金一项。

报告摘要：Computing a flow system a number of times with different samples of flow parameters is a common practice in many uncertainty quantification (UQ) applications, which can be prohibitively expensive for complex nonlinear flow problems. This talk presents two second order, stabilized, scalar auxiliary variable (SAV) ensemble algorithms for fast computation of the Navier-Stokes flow ensembles. The exiting SAV approach for the Navier-Stokes equations for a single realization has low accuracy that compromises its stability for several commonly tested benchmark flow problems. We address this issue by adding a stabilization term in each algorithm. For a single realization, both algorithms are unconditionally stable and have better accuracy than existing SAV methods.