Unconditionally stable explicit exponential methods for the Klein–Gordon–Schrödinger equations

克莱恩-戈登方程 数学 指数函数 应用数学 数学物理 数学分析 物理 非线性系统 量子力学
作者
Lijie Mei,Xiangqing Liu,Yao‐Lin Jiang
出处
期刊:Journal of Computational Physics [Elsevier BV]
卷期号:533: 113993-113993 被引量:2
标识
DOI:10.1016/j.jcp.2025.113993
摘要

In this paper, we present a framework to derive unconditionally stable explicit exponential methods for the coupled Klein–Gordon–Schrödinger (KGS) equations. The approach is based on the Hamiltonian or operator splitting. By splitting the KGS equations into three independently linear equations and solving these equations exactly with exponential methods after suitable spatial discretization, two kinds of explicit exponential methods are obtained, which could be of any order accuracy in time. It is proved that the proposed methods are time-symmetric, unconditionally stable, and mass-preserving. In particular, the derived Hamiltonian-splitting methods are symplectic and thus nearly preserve the energy. The convergence of the second-order (in time) methods is also proved. Moreover, we present a fast implementation with the Fast Fourier Transform (FFT) technique once periodic boundary conditions are prescribed for the KGS equations. Finally, 1D and 2D KGS equations are tested with the second-order and fourth-order (in time) methods. Numerical results demonstrate the high efficiency, unconditional stability with the independence of the mesh ratio, good energy and mass conservation, and applicability of large time stepsizes of the methods proposed in this paper. • A framework to derive explicit exponential methods is developed for the Klein–Gordon–Schrödinger (KGS) equations. • The new methods are time-symmetric, unconditionally stable, mass-preserving, and of any order in time. • Numerical experiments show the independence of the mesh ratio, high efficiency, and applicability of large time stepsizes.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
木木木发布了新的文献求助10
1秒前
无限鼠标完成签到,获得积分10
5秒前
5秒前
5秒前
7秒前
lee发布了新的文献求助10
8秒前
英俊的铭应助爱学习的66采纳,获得10
9秒前
来了完成签到,获得积分10
10秒前
科研通AI6.4应助哒丝萌德采纳,获得10
10秒前
tapekit完成签到,获得积分10
10秒前
张志超完成签到,获得积分10
10秒前
花砸完成签到,获得积分10
11秒前
wawa发布了新的文献求助10
12秒前
12秒前
16秒前
汉堡包应助vivi采纳,获得10
16秒前
wanci应助lee采纳,获得10
17秒前
17秒前
852应助深情的新儿采纳,获得10
19秒前
苏安泠发布了新的文献求助10
19秒前
饱满以松完成签到 ,获得积分10
22秒前
24秒前
Lucas应助ATOM采纳,获得10
24秒前
24秒前
晕晕晕发布了新的文献求助10
25秒前
机智的小霸王完成签到,获得积分10
25秒前
27秒前
开心魔镜完成签到,获得积分10
29秒前
科研通AI6.4应助crash采纳,获得10
30秒前
30秒前
荔枝发布了新的文献求助10
30秒前
共享精神应助ccc采纳,获得10
31秒前
32秒前
Isla发布了新的文献求助10
33秒前
小亮哈哈发布了新的文献求助10
34秒前
35秒前
佳佳发布了新的文献求助20
35秒前
35秒前
木木木发布了新的文献求助10
36秒前
马邦德完成签到,获得积分10
37秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Environmental Leverage in Times of Climate Crisis: Product Standards, Carbon Border Measures and Preferential Trade Agreements 1000
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
Dynamische Polarisation von H-1 und B-11 in (CH-3)-3NBH-3 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7221770
求助须知:如何正确求助?哪些是违规求助? 8851315
关于积分的说明 18677763
捐赠科研通 6880223
什么是DOI,文献DOI怎么找? 3187229
关于科研通互助平台的介绍 2351371
邀请新用户注册赠送积分活动 2161448