江苏省应用数学（中国矿业大学）中心系列学术报告
报告题目：Tamed stochastic Runge-Kutta-Chebyshev methods for stochastic differential equations with non-globally Lipschitz coefficients
报告人：唐晓 （湘潭大学）
报告时间：2024年12月12日（周四）下午13:30-16:00
报告地点：腾讯会议ID：133-403-167
报告摘要：In this talk, we introduce a new class of explicit numerical methods called the tamed stochastic Runge-Kutta-Chebyshev (t-SRKC) methods, which apply the idea of taming to the stochastic Runge-Kutta-Chebyshev (SRKC) methods. The key advantage of our explicit methods is that they can be suitable for stochastic differential equations with non-globally Lipschitz coefficients and stiffness. Under certain non-globally Lipschitz conditions, we study the strong convergence of our methods and prove that the order of strong convergence is 1/2. To show the advantages of our methods, we compare them with some existing explicit methods (including the Euler-Maruyama method, balanced Euler-Maruyama method and two types of SRKC methods) through several numerical examples. The numerical results show that our t-SRKC methods are efficient, especially for stiff stochastic differential equations..
报告人简介：唐晓，湘潭大学数学与计算科学学院副教授。研究方向为(随机)微分方程数值方法，主持国家自然科学基金青年项目1项、湖南省自然科学基金青年项目1项, 在IMA Journal of Numerical Analysis, BIT Numerical Mathematics以及Journal of Computational Mathematics等计算数学领域SCI期刊上发表学术论文10余篇。