一、主講人介紹：Akil Narayan

Akil Narayan副教授于2009年在美國(guó)布朗大學(xué)應(yīng)用數(shù)學(xué)系獲得博士學(xué)位，畢業(yè)后在普渡大學(xué)從事博士后研究，先后在馬薩諸塞大學(xué)達(dá)特茅斯分校和猶他大學(xué)擔(dān)任助理教授和副教授等教職，已在SISC、JCP、JSC等國(guó)際著名期刊上發(fā)表論文60余篇，主持DMS和NSF等基金項(xiàng)目10項(xiàng)，擔(dān)任SISC、IJUQ等國(guó)際著名期刊的編委。

二、講座信息

講座摘要：

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.

講座時(shí)間：6月16日09:00-10:00

騰訊會(huì)議號(hào)：861 344 413

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國(guó)際合作與交流處

數(shù)學(xué)科學(xué)學(xué)院 

2022年6月14日