报告人：王小捷  （中南大学  教授）

报告人简介: 王小捷，中南大学37000cm威尼斯教授，博士生导师，主要研究方向为随机偏微分方程、随机常微分方程数值方法及计算金融等。在随机常、偏微分方程数值算法与理论方面做出一系列研究成果，相关论文发表在SIAM Journal on Numerical Analysis、Mathematics of Computation、SIAM Journal on Scientific Computing、IMA Journal of Numerical Analysis、BIT Numerical Mathematics、Journal of Scientific Computing、Stochastic Processes and their Applications等享有国际学术声誉的计算数学或概率论国际主流刊物。现主持2项国家自然科学基金面上项目，1项湖南省自科杰出青年基金，主持完成1项国家自然科学基金青年项目。

报告题目：An efficient explicit full-discrete scheme for strong approximations of space-time white noise driven SPDEs with polynomial nonlinearity

报告摘要：The talk focuses on a kind of accelerated exponential Euler-type time-stepping scheme for parabolic stochastic partial differential equation with additive space-time white noise and polynomial nonlinearity. The approximation is easily implementable, performing the spatial discretization by a spectral Galerkin method and the temporal discretization by a kind of nonlinearity-tamed accelerated exponential integrator scheme. Strong convergence rates of order almost 1/2 both in space and in time is obtained for the proposed scheme. It turns out that the obtained convergence rate of the new scheme is, in the temporal direction, twice as high as existing ones in the literature. Numerical results are finally reported to confirm the previous theoretical findings.

报告时间: 2020年11月10日14:00-16:00

报告地点: 腾讯会议室：ID 434 672 1949