Improving the accuracy and consistency of the energy quadratization method with an energy-optimized technique

IF 2.1 2区 数学 Q1 MATHEMATICS, APPLIED
Xiaoqing Meng , Aijie Cheng , Zhengguang Liu
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引用次数: 0

Abstract

We propose an energy-optimized invariant energy quadratization method to solve the gradient flow models in this paper, which requires only one linear energy-optimized step to correct the auxiliary variables on each time step. In addition to inheriting the benefits of the baseline and relaxed invariant energy quadratization method, our approach has several other advantages. Firstly, in the process of correcting auxiliary variables, we can directly solve linear programming problem by the energy-optimized technique, which greatly simplifies the nonlinear optimization problem in the previous relaxed invariant energy quadratization method. Secondly, we construct new linear unconditionally energy stable schemes by applying backward differentiation formulas and Crank–Nicolson formula, so that the accuracy in time can reach the first- and second-order. Thirdly, comparing with relaxation technique, the modified energy obtained by energy-optimized technique is closer to the original energy, and the accuracy and consistency of the numerical solutions can be improved. Ample numerical examples have been presented to demonstrate the accuracy, efficiency and energy stability of the proposed schemes.
用能量优化技术提高能量四分法的准确性和一致性
我们在本文中提出了一种能量优化的不变能量四分法来求解梯度流模型,它只需要一个线性能量优化步骤来修正每个时间步上的辅助变量。除了继承基线和松弛不变能量四分法的优点外,我们的方法还具有其他几个优点。首先,在修正辅助变量的过程中,我们可以通过能量优化技术直接求解线性规划问题,这大大简化了以往松弛不变能量四分法中的非线性优化问题。其次,我们应用后向微分公式和 Crank-Nicolson 公式构建了新的线性无条件能量稳定方案,使时间精度达到一阶和二阶。第三,与松弛技术相比,能量优化技术得到的修正能量更接近原始能量,数值解的精度和一致性也得到提高。大量的数值实例证明了所提方案的精度、效率和能量稳定性。
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来源期刊
CiteScore
5.40
自引率
4.20%
发文量
437
审稿时长
3.0 months
期刊介绍: The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.
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