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arXiv 2106.14744math.NAcs.NA
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Cahn-Hilliard方程的$C^0$内罚方法的连续数据同化和长时间精度

Continuous data assimilation and long-time accuracy in a $C^0$ interior penalty method for the Cahn-Hilliard equation

Amanda E. Diegel, Leo G. Rebholz

摘要:

我们提出了一个数值逼近方法的Cahn-Hilliard方程,采用连续数据同化,以达到长时间的精度。该方法使用C $^0$内部惩罚空间离散的四阶Cahn-Hilliard方程,以及向后欧拉时间离散。我们证明了该方法是长期稳定和长期准确的,任意不准确的初始条件,提供足够的数据测量纳入模拟。数值实验表明了该方法在基准测试问题上的有效性。

英文摘要:

We propose a numerical approximation method for the Cahn-Hilliard equations that incorporates continuous data assimilation in order to achieve long time accuracy. The method uses a C$^0$ interior penalty spatial discretization of the fourth order Cahn-Hilliard equations, together with a backward Euler temporal discretization. We prove the method is long time stable and long time accurate, for arbitrarily inaccurate initial conditions, provided enough data measurements are incorporated into the simulation. Numerical experiments illustrate the effectiveness of the method on a benchmark test problem.

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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