龙格-库塔方法
数学
离散化
常微分方程的数值方法
放松(心理学)
常微分方程
偏微分方程
理论(学习稳定性)
应用数学
单调函数
显式和隐式方法
数值分析
线性多步法
微分方程
L-稳定性
数学分析
微分代数方程
计算机科学
社会心理学
心理学
机器学习
作者
Dongfang Li,Xiaoxi Li,Zhimin Zhang
摘要
Spatial discretizations of time-dependent partial differential equations usually result in a large system of semi-linear and stiff ordinary differential equations. Taking the structures into account, we develop a family of linearly implicit and high order accurate schemes for the time discretization, using the idea of implicit-explicit Runge-Kutta methods and the relaxation techniques. The proposed schemes are monotonicity-preserving/conservative for the original problems, while the previous linearized methods are usually not. We also discuss the linear stability and strong stability preserving (SSP) property of the new relaxation methods. Numerical experiments on several typical models are presented to confirm the effectiveness of the proposed methods.
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