一、主讲人介绍：Akil Narayan

Akil Narayan副教授于2009年在美国布朗大学应用数学系获得博士学位，毕业后在普渡大学从事博士后研究，先后在马萨诸塞大学达特茅斯分校和犹他大学担任助理教授和副教授等教职，已在SISC、JCP、JSC等国际著名期刊上发表论文60余篇，主持DMS和NSF等基金项目10项，担任SISC、IJUQ等国际著名期刊的编委。

二、讲座信息

讲座摘要：

In practice, the environment or initial conditions of shallow water equations (SWE) may be imprecisely known due to incomplete information, or uncertain. One effective strategy for propagating this input uncertainty forward through the SWE is the stochastic Galerkin method via polynomial Chaos. An outstanding challenge with numerical methods arising from this approach is that the model may lose important physical structure of the solution. We show that a known elegant connection in the deterministic case between water height positivity and hyperbolicity of the equations can be extended to the stochastic/uncertain case. Our algorithms ensure positivity of the water height, hyperbolicity of the stochastic Galerkin formulation, and obey the well-balanced property, ensuring stable simulation of certain steady-state solutions. We demonstate the effectiveness of the algorithm for simulations in one and two spatial dimensions.

讲座时间：6月16日09:00-10:00

腾讯会议号：861 344 413

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国际合作与交流处

数学科学学院 

2022年6月14日