数学系Seminar第1737期 反散射变换数值格式求解非线性发展方程及其在时间分数阶微分方程中的应用

创建时间:  2018/12/11  龚惠英   浏览次数:   返回

报告主题:反散射变换数值格式求解非线性发展方程及其在时间分数阶微分方程中的应用
报告人:Thiab Taha   教授  (美国University of Georgia)
报告时间:2018年12月12日(周三)15:00
报告地点:校本部G507
邀请人:李常品
主办部门:太阳成集团tyc33455数学系
报告摘要:In this talk, a survey and a method of derivation of certain class of numerical schemes and an implementation of these schemes will be presented. These schemes are constructed by methods related to the Inverse Scattering Transform (IST) and can be used as numerical schemes for their associated nonlinear evolution equations. They maintain many of the important properties of their original partial differential equations such as infinite numbers of conservation laws and solvability by IST. Numerical experiments have shown that these schemes compare very favorably with other known numerical methods.

 

 


欢迎教师、员工参加!

上一条:太阳集团tyc33455核心数学讲座第三讲 代数的世界

下一条:物理学科Seminar第454讲 Revealing underlying principle and molecular basis of tumorigenesis by combining quantitative model and large-scale cancer data


数学系Seminar第1737期 反散射变换数值格式求解非线性发展方程及其在时间分数阶微分方程中的应用

创建时间:  2018/12/11  龚惠英   浏览次数:   返回

报告主题:反散射变换数值格式求解非线性发展方程及其在时间分数阶微分方程中的应用
报告人:Thiab Taha   教授  (美国University of Georgia)
报告时间:2018年12月12日(周三)15:00
报告地点:校本部G507
邀请人:李常品
主办部门:太阳成集团tyc33455数学系
报告摘要:In this talk, a survey and a method of derivation of certain class of numerical schemes and an implementation of these schemes will be presented. These schemes are constructed by methods related to the Inverse Scattering Transform (IST) and can be used as numerical schemes for their associated nonlinear evolution equations. They maintain many of the important properties of their original partial differential equations such as infinite numbers of conservation laws and solvability by IST. Numerical experiments have shown that these schemes compare very favorably with other known numerical methods.

 

 


欢迎教师、员工参加!

上一条:太阳集团tyc33455核心数学讲座第三讲 代数的世界

下一条:物理学科Seminar第454讲 Revealing underlying principle and molecular basis of tumorigenesis by combining quantitative model and large-scale cancer data