arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2204.06258math.NAcs.NAmath.AP
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梯度流的高效指数标量松弛辅助变量法(RE-SAV)

Highly efficient exponential scalar auxiliary variable approaches with relaxation (RE-SAV) for gradient flows

Zhengguang Liu, Xiaoli Li

摘要:

在过去的几年里,标量辅助变量(SAV)和SAV型方法成为非常热门和有效的方法来模拟各种梯度流。受\cite{huang2020highly}中新的SAV方法的启发,我们提出了一种新的构造指数标量辅助变量(E-SAV)方法的技术,用于构造梯度流的高阶数值能量稳定格式.为了显著提高算法的精度和一致性,我们提出了一种带松弛的E-SAV算法,我们称之为梯度流松弛E-SAV(RE-SAV)方法。RE-SAV方法保留了传统SAV方法的所有优点。另外,我们不需要对自由能势或非线性项作任何有界自下而上的假设。此外,一阶、二阶和高阶无条件能量稳定的时间推进格式易于构造。数值算例证明了该方法的有效性和准确性。

英文摘要:

For the past few years, scalar auxiliary variable (SAV) and SAV-type approaches became very hot and efficient methods to simulate various gradient flows. Inspired by the new SAV approach in \cite{huang2020highly}, we propose a novel technique to construct a new exponential scalar auxiliary variable (E-SAV) approach to construct high-order numerical energy stable schemes for gradient flows. To improve its accuracy and consistency noticeably, we propose an E-SAV approach with relaxation, which we named the relaxed E-SAV (RE-SAV) method for gradient flows. The RE-SAV approach preserves all the advantages of the traditional SAV approach. In addition, we do not need any the bounded-from-below assumptions for the free energy potential or nonlinear term. Besides, the first-order, second-order and higher-order unconditionally energy stable time-stepping schemes are easy to construct. Several numerical examples are provided to demonstrate the improved efficiency and accuracy of the proposed method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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