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Efficient Numerical Methods for Computing the Stationary States of Phase Field Crystal Models
SIAM Journal on Scientific Computing ( IF 3.0 ) Pub Date : 2020-11-09 , DOI: 10.1137/20m1321176
Kai Jiang , Wei Si , Chang Chen , Chenglong Bao

SIAM Journal on Scientific Computing, Volume 42, Issue 6, Page B1350-B1377, January 2020.
Finding the stationary states of a free energy functional is an important problem in phase field crystal (PFC) models. Many efforts have been devoted to designing numerical schemes with energy dissipation and mass conservation properties. However, most existing approaches are time-consuming due to the requirement of small effective step sizes. In this paper, we discretize the energy functional and propose efficient numerical algorithms for solving the constrained nonconvex minimization problem. A class of gradient-based approaches, which are the so-called adaptive accelerated Bregman proximal gradient (AA-BPG) methods, is proposed, and the convergence property is established without the global Lipschitz constant requirements. A practical Newton method is also designed to further accelerate the local convergence with convergence guarantee. One key feature of our algorithms is that the energy dissipation and mass conservation properties hold during the iteration process. Moreover, we develop a hybrid acceleration framework to accelerate the AA-BPG methods and most of the existing approaches through coupling with the practical Newton method. Extensive numerical experiments, including two three-dimensional periodic crystals in the Landau--Brazovskii (LB) model and a two-dimensional quasicrystal in the Lifshitz--Petrich (LP) model, demonstrate that our approaches have adaptive step sizes which lead to a significant acceleration over many existing methods when computing complex structures.


中文翻译:

计算相场晶体模型稳态的高效数值方法

SIAM科学计算杂志,第42卷,第6期,第B1350-B1377页,2020年1月。
在相场晶体(PFC)模型中,找到自由能泛函的稳态是一个重要的问题。已经进行了许多努力来设计具有能量消耗和质量守恒特性的数值方案。然而,由于要求小的有效步长,大多数现有方法是耗时的。在本文中,我们离散化能量函数,并提出了有效的数值算法来解决约束非凸最小化问题。提出了一类基于梯度的方法,即所谓的自适应加速布雷格曼近端梯度(AA-BPG)方法,并且在没有全局Lipschitz常数要求的情况下建立了收敛性。还设计了一种实用的牛顿法,以在收敛保证的情况下进一步加速局部收敛。我们算法的一个关键特征是在迭代过程中保持了能量耗散和质量守恒的特性。此外,我们开发了一种混合加速框架,通过结合实用的牛顿法来加速AA-BPG方法和大多数现有方法。广泛的数值实验,包括在Landau-Brazovskii(LB)模型中的两个三维周期晶体和在Lifshitz-Petrich(LP)模型中的二维准晶体,证明我们的方法具有自适应的步长,从而导致计算复杂结构时,与许多现有方法相比有明显的加速。我们开发了一种混合加速框架,通过与实用的牛顿法相结合来加速AA-BPG方法和大多数现有方法。广泛的数值实验,包括在Landau-Brazovskii(LB)模型中的两个三维周期晶体和在Lifshitz-Petrich(LP)模型中的二维准晶体,证明我们的方法具有自适应的步长,从而导致计算复杂结构时,与许多现有方法相比有明显的加速。我们开发了一种混合加速框架,通过与实用的牛顿法相结合来加速AA-BPG方法和大多数现有方法。广泛的数值实验,包括在Landau-Brazovskii(LB)模型中的两个三维周期晶体和在Lifshitz-Petrich(LP)模型中的二维准晶体,证明我们的方法具有自适应的步长,从而导致计算复杂结构时,与许多现有方法相比有明显的加速。
更新日期:2020-12-04
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