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arXiv 2004.15025math.PR
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Lipschitz随机曲面的宏观行为

Macroscopic behavior of Lipschitz random surfaces

Piet Lammers, Martin Tassy

AI总结:

本文研究Lipschitz随机曲面表面张力的严格凸性,纳入长程与无限程相互作用模型,证明单调性条件下表面张力严格凸并解决相关猜想与问题,还得到比自由能性质、大偏差原理等辅助结果。

摘要:

本文的研究动机是推导Lipschitz随机曲面(即从$\mathbb Z^d$到$\mathbb Z$或$\mathbb R$的随机Lipschitz函数模型)表面张力的严格凸性。一项核心创新在于,本分析纳入了具有长程和无限程相互作用的随机曲面模型。更具体地说,我们的研究至少覆盖:对任意$k\geq2$,从$\mathbb Z^d$到$k$正则树的均匀随机图同态,以及满足FKG格条件的Lipschitz势函数。后者涵盖dimer模型、六顶点模型的扰动,以及Sheffield提出的Lipschitz简单吸引势的扰动。我们的主要结果是,若所研究的模型在边界条件下具有单调性,则可证明其表面张力的严格凸性——这意味着极限宏观轮廓的唯一性。该结果解决了Menz和Tassy的一个猜想,并回答了Sheffield提出的一个问题。作为辅助成果,我们证明了若干可能具有独立研究价值的结果,这些结果不依赖于模型的单调性,包括比自由能的存在性和拓扑性质,以及对其极小值的刻画。我们还证明了一个通用的大偏差原理,该原理可同时描述高度函数的宏观轮廓和局部统计特性。本工作受Sheffield所著《Random Surfaces》的启发,但独立于该著作。

英文摘要:

The motivation for this article is to derive strict convexity of the surface tension for Lipschitz random surfaces, that is, for models of random Lipschitz functions from $\mathbb Z^d$ to $\mathbb Z$ or $\mathbb R$. An essential innovation is that random surface models with long- and infinite-range interactions are included in the analysis. More specifically, we cover at least: uniformly random graph homomorphisms from $\mathbb Z^d$ to a $k$-regular tree for any $k\geq 2$ and Lipschitz potentials which satisfy the FKG lattice condition. The latter includes perturbations of dimer- and six-vertex models and of Lipschitz simply attractive potentials introduced by Sheffield. The main result is that we prove strict convexity of the surface tension -- which implies uniqueness for the limiting macroscopic profile -- if the model of interest is monotone in the boundary conditions. This solves a conjecture of Menz and Tassy, and answers a question posed by Sheffield. Auxiliary to this, we prove several results which may be of independent interest, and which do not rely on the model being monotone. This includes existence and topological properties of the specific free energy, as well as a characterization of its minimizers. We also prove a general large deviations principle which describes both the macroscopic profile and the local statistics of the height functions. This work is inspired by, but independent of, Random Surfaces by Sheffield.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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