Superconcentration in surface growth

Superconcentration in surface growth
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表面生长超浓缩

DOI:
10.1002/rsa.21108
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发表时间:
2023
影响因子:
1
通讯作者:
Chatterjee, Sourav
Chatterjee, Sourav
中科院分区:
数学3区
文献类型:
--
作者:
Chatterjee, Sourav

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增长随机表面的高度函数通常被证明是超集中的,这意味着它们的方差随时间呈次线性增长。本文介绍了一个新的概念-所谓的subroughness-的意思是存在两个不同的点,使期望的平方差之间的高度在这些点的时间增长亚线性。本文的主要结果是:在一类生长随机曲面中,超密集等价于亚粗糙。结果被应用于建立超浓缩在一个变体的限制固体上固体(RSOS)模型和弹道沉积模型的变体,并给出新的证明超浓缩定向末道渗流和定向聚合物。
Height functions of growing random surfaces are often conjectured to be superconcentrated, meaning that their variances grow sublinearly in time. This article introduces a new concept—calledsubroughness—meaning that there exist two distinct points such that the expected squared difference between the heights at these points grows sublinearly in time. The main result of the paper is that superconcentration is equivalent to subroughness in a class of growing random surfaces. The result is applied to establish superconcentration in a variant of the restricted solid‐on‐solid (RSOS) model and in a variant of the ballistic deposition model, and give new proofs of superconcentration in directed last‐passage percolation and directed polymers.
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