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引用次数: 0
摘要
我们分析了随机热方程(SHE)的高矩,将其转换为有吸引力的布朗粒子(BPs),即通过成对吸引力漂移相互作用的布朗运动。在粒子趋于聚集的缩放状态下,我们证明了吸引力布朗粒子经验度量的大偏差原理(LDP)。在 delta(-like)初始条件下,我们描述了速率函数的唯一最小值,并将该最小值与 Kardar-Parisi-Zhang (KPZ) 方程在上尾部的时空极限形状联系起来。本文的结果被用在同行论文[75]中,证明了 KPZ 方程的 n 点上尾 LDP,并描述了相应的时空极限形状。
We analyze the high moments of the Stochastic Heat Equation (SHE) via a transformation to the attractive Brownian Particles (BPs), which are Brownian motions interacting via pairwise attractive drift. In those scaling regimes where the particles tend to cluster, we prove a Large Deviation Principle (LDP) for the empirical measure of the attractive BPs. Under the delta(-like) initial condition, we characterize the unique minimizer of the rate function and relate the minimizer to the spacetime limit shapes of the Kardar–Parisi–Zhang (KPZ) equation in the upper tails. The results of this paper are used in the companion paper [75] to prove an n-point, upper-tail LDP for the KPZ equation and to characterize the corresponding spacetime limit shape.
期刊介绍:
The Journal of Functional Analysis presents original research papers in all scientific disciplines in which modern functional analysis plays a basic role. Articles by scientists in a variety of interdisciplinary areas are published.
Research Areas Include:
• Significant applications of functional analysis, including those to other areas of mathematics
• New developments in functional analysis
• Contributions to important problems in and challenges to functional analysis