论文标题
随机环境中的一维聚合物:拉伸与折叠
One-dimensional polymers in random environments: stretching vs. folding
论文作者
论文摘要
在本文中,我们在$ \ Mathbb z $上研究了A \ Emph {非导向聚合物模型},这是在随机环境中放置的一维简单随机步行。 More precisely, the law of the random walk is modified by the exponential of the sum of "rewards" (or penalities) $βω_x -h$ sitting on the range of the random walk, where $(ω_x)_{x\in \mathbb Z}$ are i.i.d.\ random variables (the disorder), and where $β\geq 0$ (disorder strength) and $h\in \ mathbb {r} $(外部字段)是两个参数。当$β= 0,h> 0 $时,这对应于其范围内的随机行走;当$β> 0,h = 0 $时,这对应于随机环境中的“标准”聚合物模型,除了未指导。在这项工作中,我们允许参数$β,h $根据随机步行的长度而变化,并且我们详细研究了该疾病的\ emph {straption效应}之间的竞争,外部字段的\ emph {折叠效应}(如果$ h \ ge 0 $)和\ emph {entropy contopy contopy contropy contopery的竞争。我们证明了(富)相图的完整描述。例如,在$β> 0的情况下,非导向聚合物的H = 0 $,如果$ω_x$ ha有限的第二刻,我们发现横向波动指数$ $ξ= 2/3 $,并且我们确定了重新分配的日志分区功能的有限分布。
In this article we study a \emph{non-directed polymer model} on $\mathbb Z$, that is a one-dimensional simple random walk placed in a random environment. More precisely, the law of the random walk is modified by the exponential of the sum of "rewards" (or penalities) $βω_x -h$ sitting on the range of the random walk, where $(ω_x)_{x\in \mathbb Z}$ are i.i.d.\ random variables (the disorder), and where $β\geq 0$ (disorder strength) and $h\in \mathbb{R}$ (external field) are two parameters. When $β=0,h>0$, this corresponds to a random walk penalized by its range; when $β>0, h=0$, this corresponds to the "standard" polymer model in random environment, except that it is non-directed. In this work, we allow the parameters $β,h$ to vary according to the length of the random walk, and we study in detail the competition between the \emph{stretching effect} of the disorder, the \emph{folding effect} of the external field (if $h\ge 0$), and the \emph{entropy cost} of atypical trajectories. We prove a complete description of the (rich) phase diagram. For instance, in the case $β>0, h=0$ of the non-directed polymer, if $ω_x$ ha a finite second moment, we find a transversal fluctuation exponent $ξ=2/3$, and we identify the limiting distribution of the rescaled log-partition function.