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A second‐order energy stable backward differentiation formula method for the epitaxial thin film equation with slope selection
Author(s) -
Feng Wenqiang,
Wang Cheng,
Wise Steven M.,
Zhang Zhengru
Publication year - 2018
Publication title -
numerical methods for partial differential equations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.901
H-Index - 61
eISSN - 1098-2426
pISSN - 0749-159X
DOI - 10.1002/num.22271
Subject(s) - mathematics , stability (learning theory) , nonlinear system , convergence (economics) , energy (signal processing) , mathematical analysis , nonlinear conjugate gradient method , mathematical optimization , gradient descent , computer science , statistics , physics , quantum mechanics , machine learning , economics , economic growth , artificial neural network
In this article, we study a new second‐order energy stable Backward Differentiation Formula (BDF) finite difference scheme for the epitaxial thin film equation with slope selection (SS). One major challenge for higher‐order‐in‐time temporal discretizations is how to ensure an unconditional energy stability without compromising numerical efficiency or accuracy. We propose a framework for designing a second‐order numerical scheme with unconditional energy stability using the BDF method with constant coefficient stabilizing terms. Based on the unconditional energy stability property that we establish, we derive anℓ ∞ ( 0 , T ; H h 2 ) stability for the numerical solution and provide an optimal convergence analysis. To deal with the highly nonlinear four‐Laplacian term at each time step, we apply efficient preconditioned steepest descent and preconditioned nonlinear conjugate gradient algorithms to solve the corresponding nonlinear system. Various numerical simulations are presented to demonstrate the stability and efficiency of the proposed schemes and solvers. Comparisons with other second‐order schemes are presented.