Numerical approximation of the stochastic Cahn-Hilliard equation near the sharp interface limit
| Autor: | Robert Nürnberg, Lubomir Banas, Andreas Prohl, D. C. Antonopoulou |
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| Rok vydání: | 2019 |
| Předmět: |
math.NA
Discretization Applied Mathematics Numerical analysis Mathematical analysis Numerical Analysis (math.NA) 010103 numerical & computational mathematics 01 natural sciences Finite element method Physics::Fluid Dynamics 010101 applied mathematics Computational Mathematics Convergence of random variables Iterated function FOS: Mathematics Mathematics - Numerical Analysis Limit (mathematics) 0101 mathematics Balanced flow Cahn–Hilliard equation Nonlinear Sciences::Pattern Formation and Solitons Mathematics |
| Popis: | We consider the stochastic Cahn–Hilliard equation with additive noise term $$\varepsilon ^\gamma g\, {\dot{W}}$$ ε γ g W ˙ ($$\gamma >0$$ γ > 0 ) that scales with the interfacial width parameter $$\varepsilon $$ ε . We verify strong error estimates for a gradient flow structure-inheriting time-implicit discretization, where $$\varepsilon ^{-1}$$ ε - 1 only enters polynomially; the proof is based on higher-moment estimates for iterates, and a (discrete) spectral estimate for its deterministic counterpart. For $$\gamma $$ γ sufficiently large, convergence in probability of iterates towards the deterministic Hele–Shaw/Mullins–Sekerka problem in the sharp-interface limit $$\varepsilon \rightarrow 0$$ ε → 0 is shown. These convergence results are partly generalized to a fully discrete finite element based discretization. We complement the theoretical results by computational studies to provide practical evidence concerning the effect of noise (depending on its ’strength’ $$\gamma $$ γ ) on the geometric evolution in the sharp-interface limit. For this purpose we compare the simulations with those from a fully discrete finite element numerical scheme for the (stochastic) Mullins–Sekerka problem. The computational results indicate that the limit for $$\gamma \ge 1$$ γ ≥ 1 is the deterministic problem, and for $$\gamma =0$$ γ = 0 we obtain agreement with a (new) stochastic version of the Mullins–Sekerka problem. |
| Databáze: | OpenAIRE |
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