Mathey Steven, Agoritsas Elisabeth, Kloss Thomas, Lecomte Vivien, Canet Léonie
LPMMC, Université Grenoble Alpes, and CNRS, 38042 Grenoble, France.
LIPhy, Université Grenoble Alpes, and CNRS, 38042 Grenoble, France.
Phys Rev E. 2017 Mar;95(3-1):032117. doi: 10.1103/PhysRevE.95.032117. Epub 2017 Mar 7.
We investigate the stationary-state fluctuations of a growing one-dimensional interface described by the Kardar-Parisi-Zhang (KPZ) dynamics with a noise featuring smooth spatial correlations of characteristic range ξ. We employ nonperturbative functional renormalization group methods to resolve the properties of the system at all scales. We show that the physics of the standard (uncorrelated) KPZ equation emerges on large scales independently of ξ. Moreover, the renormalization group flow is followed from the initial condition to the fixed point, that is, from the microscopic dynamics to the large-distance properties. This provides access to the small-scale features (and their dependence on the details of the noise correlations) as well as to the universal large-scale physics. In particular, we compute the kinetic energy spectrum of the stationary state as well as its nonuniversal amplitude. The latter is experimentally accessible by measurements at large scales and retains a signature of the microscopic noise correlations. Our results are compared to previous analytical and numerical results from independent approaches. They are in agreement with direct numerical simulations for the kinetic energy spectrum as well as with the prediction, obtained with the replica trick by Gaussian variational method, of a crossover in ξ of the nonuniversal amplitude of this spectrum.
我们研究了由 Kardar-Parisi-Zhang(KPZ)动力学描述的一维生长界面的稳态涨落,该动力学具有特征范围为 ξ 的平滑空间关联噪声。我们采用非微扰泛函重整化群方法来解析系统在所有尺度下的性质。我们表明,标准(无关联)KPZ 方程的物理特性在大尺度上出现,且与 ξ 无关。此外,重整化群流从初始条件追踪到不动点,即从微观动力学追踪到远距离性质。这使得我们能够了解小尺度特征(及其对噪声关联细节的依赖性)以及普遍的大尺度物理。特别地,我们计算了稳态的动能谱及其非普适振幅。后者可通过大尺度测量在实验上获取,并保留了微观噪声关联的特征。我们的结果与之前独立方法得到的解析和数值结果进行了比较。它们与动能谱的直接数值模拟结果一致,也与通过高斯变分法的 replica 技巧得到的关于该谱非普适振幅在 ξ 中的交叉点的预测一致。