Γ-convergence和相变泛函的随机均匀化

IF 1.3 3区 数学 Q4 AUTOMATION & CONTROL SYSTEMS Esaim-Control Optimisation and Calculus of Variations Pub Date : 2022-06-27 DOI:10.1051/cocv/2023030
R. Marziani
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引用次数: 4

摘要

本文研究了奇异摄动相变泛函的渐近性,其形式为:(1/εk∫Afk(∈,u,εk∇u)d,其中,u∈[0,1]是相场变量,εk > 0是奇异摄动参数,即εk→0,当k→+∞时,对于每一个x和每一个k, fk(x,·,0)是一个在0和1处为零的双阱势。我们证明了一个曲面泛函的函数Fk Γ-converge(不超过子序列)的形式为:∞(u) =∫Su∩Af∞(s1,𝜈u)dHn-1,其中u∈BV(a;{0,1})和f∞的特征是适当缩放最小化问题的双极限。然后,我们将我们的分析扩展到随机均匀化的设置,并证明了平稳随机积分的Γ-convergence结果。
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Γ-convergence and stochastic homogenisation of phase-transition functionals
In this paper, we study the asymptotics of singularly perturbed phase-transition functionals of the form ℱk(u) = 1/εk∫Afk(𝑥,u,εk∇u)d𝑥, where u ∈ [0, 1] is a phase-field variable, εk > 0 a singular-perturbation parameter i.e., εk → 0, as k → +∞, and the integrands fk are such that, for every x and every k, fk(x, ·, 0) is a double well potential with zeros at 0 and 1. We prove that the functionals Fk Γ-converge (up to subsequences) to a surface functional of the form ℱ∞(u) = ∫Su∩Af∞(𝑥,𝜈u)dHn-1, where u ∈ BV(A; {0, 1}) and f∞ is characterised by the double limit of suitably scaled minimisation problems. Afterwards we extend our analysis to the setting of stochastic homogenisation and prove a Γ-convergence result for stationary random integrands.
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来源期刊
Esaim-Control Optimisation and Calculus of Variations
Esaim-Control Optimisation and Calculus of Variations Mathematics-Computational Mathematics
自引率
7.10%
发文量
77
期刊介绍: ESAIM: COCV strives to publish rapidly and efficiently papers and surveys in the areas of Control, Optimisation and Calculus of Variations. Articles may be theoretical, computational, or both, and they will cover contemporary subjects with impact in forefront technology, biosciences, materials science, computer vision, continuum physics, decision sciences and other allied disciplines. Targeted topics include: in control: modeling, controllability, optimal control, stabilization, control design, hybrid control, robustness analysis, numerical and computational methods for control, stochastic or deterministic, continuous or discrete control systems, finite-dimensional or infinite-dimensional control systems, geometric control, quantum control, game theory; in optimisation: mathematical programming, large scale systems, stochastic optimisation, combinatorial optimisation, shape optimisation, convex or nonsmooth optimisation, inverse problems, interior point methods, duality methods, numerical methods, convergence and complexity, global optimisation, optimisation and dynamical systems, optimal transport, machine learning, image or signal analysis; in calculus of variations: variational methods for differential equations and Hamiltonian systems, variational inequalities; semicontinuity and convergence, existence and regularity of minimizers and critical points of functionals, relaxation; geometric problems and the use and development of geometric measure theory tools; problems involving randomness; viscosity solutions; numerical methods; homogenization, multiscale and singular perturbation problems.
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