Entropy-dissipating finite-difference schemes for nonlinear fourth-order parabolic equations

Marcel Braukhoff; Ansgar Jüngel

doi:10.3934/dcdsb.2020234

Article Contents

2021, Volume 26, Issue 6: 3335-3355. Doi: 10.3934/dcdsb.2020234

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Entropy-dissipating finite-difference schemes for nonlinear fourth-order parabolic equations

Marcel Braukhoff and
Ansgar Jüngel^,

Institute of Analysis and Scientific Computing, Technische Universität Wien, Wiedner Hauptstraße 8–10, 1040 Wien, Austria

^* Corresponding author: Ansgar Jüngel
^* Corresponding author: Ansgar Jüngel

Received: December 31, 2019

Revised: May 31, 2020

Early access: August 2020

Published: June 2021

The authors acknowledge partial support from the Austrian Science Fund (FWF), grants F65, P30000, P33010, and W1245

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Abstract

Structure-preserving finite-difference schemes for general nonlinear fourth-order parabolic equations on the one-dimensional torus are derived. Examples include the thin-film and the Derrida–Lebowitz–Speer–Spohn equations. The schemes conserve the mass and dissipate the entropy. The scheme associated to the logarithmic entropy also preserves the positivity. The idea of the derivation is to reformulate the equations in such a way that the chain rule is avoided. A central finite-difference discretization is then applied to the reformulation. In this way, the same dissipation rates as in the continuous case are recovered. The strategy can be extended to a multi-dimensional thin-film equation. Numerical examples in one and two space dimensions illustrate the dissipation properties.

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Mathematics Subject Classification: Primary: 35K30; Secondary: 35Q68, 65M06, 65M12.

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Figure 1. Evolution of the DLSS equation in a semi-logarithmic scale, using the initial datum $ u^0(x) = \max\{10^{-10}, \cos(\pi x)^{16}\} $

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Figure 2. Left: Decay of the logarithmic entropy $ s_0(u(t)) $ for two different space grid sizes $ h = 1/20 $ and $ h = 1/200 $. Right: Convergence of the $ \ell^2 $ error. The dots are the values from the numerical solution, the solid line is the regression curve

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Figure 3. Left: Decay of the Shannon entropy $ s_1(u(t)) $ with $ h = 1/100 $. Right: Convergence of the $ \ell^2 $ error. The dots are the values from the numerical solution, the solid line is the regression curve

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Figure 4. Evolution of the solution to the thin-film equation at times $ t = 0 $ (densely dotted), $ t = 2\cdot 10^{-4} $ (dotted), $ t = 5\cdot 10^{-4} $ (dash-dotted), $ t = 1\cdot 10^{-3} $ (dashed), $ t = 2\cdot 10^{-3} $ (densely dashed), and $ t = 5\cdot 10^{-3} $ (solid) and grid sizes $ h = 1/10 $ (left), $ h = 1/200 $ (right)

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Figure 5. Decay of the logarithmic entropy $ S_0(u(t)) $ for various space grid sizes

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Figure 6. Evolution of the solution to the two-dimensional thin-film equation with $ \beta = 2 $, $ t = 0 $ (top left), $ t = 3\cdot 10^{-9} $ (top right), $ t = 10^{-8} $ (bottom left), $ t = 10^{-6} $ (bottom right)

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Figure 7. Decay of the logarithmic entropy $ S_0(u(t)) $ for various space grid sizes

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