arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2409.19481math.NAcs.NA
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Allen-Cahn模型的高效变时间步长DLN算法

The Efficient Variable Time-stepping DLN Algorithms for the Allen-Cahn Model

YiMing Chen, Dianlun Luo, Wenlong Pei, Yulong Xing

AI总结:

本文针对Allen-Cahn方程提出一族无条件非线性稳定且二阶精确的变时间步长DLN格式,结合部分隐式修正与标量辅助变量算法,证明能量长期稳定性并给出误差分析,同时提出时间自适应算法并通过数值实验验证。

摘要:

我们考虑一族变时间步长的Dahlquist-Liniger-Nevanlinna(DLN)格式,用于Allen-Cahn方程,该格式无条件非线性稳定且具有二阶精度。空间离散采用有限元方法。对于非线性项,我们将DLN格式与两种高效时间算法相结合:部分隐式修正算法和标量辅助变量算法。对于这两种方法,我们证明了在任意时间步长序列下模型能量的无条件长期稳定性。此外,我们对变时间步长的部分隐式修正算法给出了严格的误差分析。我们还提出了基于这些格式的高效时间自适应算法。给出了若干一维和二维数值算例,以验证所提出的时间自适应DLN方法的性质。

英文摘要:

We consider a family of variable time-stepping Dahlquist-Liniger-Nevanlinna (DLN) schemes, which is unconditional non-linear stable and second order accurate, for the Allen-Cahn equation. The finite element methods are used for the spatial discretization. For the non-linear term, we combine the DLN scheme with two efficient temporal algorithms: partially implicit modified algorithm and scalar auxiliary variable algorithm. For both approaches, we prove the unconditional, long-term stability of the model energy under any arbitrary time step sequence. Moreover, we provide rigorous error analysis for the partially implicit modified algorithm with variable time-stepping. Efficient time adaptive algorithms based on these schemes are also proposed. Several one- and two-dimensional numerical tests are presented to verify the properties of the proposed time adaptive DLN methods.

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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