Length Preserving Numerical Schemes for Landau–Lifshitz Equation Based on Lagrange Multiplier Approaches

IF 3 2区 数学 Q1 MATHEMATICS, APPLIED SIAM Journal on Scientific Computing Pub Date : 2023-04-26 DOI:10.1137/22m1501143
Qing Cheng, Jie Shen
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Abstract

We develop in this paper two classes of length preserving schemes for the Landau–Lifshitz equation based on two different Lagrange multiplier approaches. In the first approach, the Lagrange multiplier equals at the continuous level, while in the second approach, the Lagrange multiplier is introduced to enforce the length constraint at the discrete level and is identically zero at the continuous level. By using a predictor-corrector approach, we construct efficient and robust length preserving higher-order schemes for the Landau–Lifshitz equation, with the computational cost dominated by the predictor step which is simply a semi-implicit scheme. Furthermore, by introducing another space-independent Lagrange multiplier, we construct energy dissipative, in addition to length preserving, schemes for the Landau–Lifshitz equation, at the expense of solving one nonlinear algebraic equation. We present ample numerical experiments to validate the stability and accuracy for the proposed schemes, and also provide a performance comparison with some existing schemes.
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基于拉格朗日乘数法的Landau-Lifshitz方程保长数值格式
本文基于两种不同的拉格朗日乘子方法,给出了Landau-Lifshitz方程的两类保长格式。在第一种方法中,拉格朗日乘子在连续水平上等于,而在第二种方法中,拉格朗日乘子在离散水平上强制长度约束,在连续水平上等于零。采用预测-校正方法,构造了Landau-Lifshitz方程的高效、鲁棒的保长高阶格式,其计算代价由预测步控制,预测步是一种简单的半隐式格式。此外,通过引入另一个与空间无关的拉格朗日乘子,我们以求解一个非线性代数方程为代价,为Landau-Lifshitz方程构造了能量耗散和长度保持格式。通过大量的数值实验验证了所提方案的稳定性和准确性,并与一些现有方案进行了性能比较。
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来源期刊
CiteScore
5.50
自引率
3.20%
发文量
209
审稿时长
1 months
期刊介绍: The purpose of SIAM Journal on Scientific Computing (SISC) is to advance computational methods for solving scientific and engineering problems. SISC papers are classified into three categories: 1. Methods and Algorithms for Scientific Computing: Papers in this category may include theoretical analysis, provided that the relevance to applications in science and engineering is demonstrated. They should contain meaningful computational results and theoretical results or strong heuristics supporting the performance of new algorithms. 2. Computational Methods in Science and Engineering: Papers in this section will typically describe novel methodologies for solving a specific problem in computational science or engineering. They should contain enough information about the application to orient other computational scientists but should omit details of interest mainly to the applications specialist. 3. Software and High-Performance Computing: Papers in this category should concern the novel design and development of computational methods and high-quality software, parallel algorithms, high-performance computing issues, new architectures, data analysis, or visualization. The primary focus should be on computational methods that have potentially large impact for an important class of scientific or engineering problems.
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