Burgers dynamics for Poisson point process initial conditions of the Weibull class

TL;DR

This paper analytically studies the statistical properties of one-dimensional Burgers dynamics with Poisson point process initial conditions, revealing self-similar evolution and stretched-exponential distributions.

Contribution

It derives explicit formulas for various statistical quantities of Burgers dynamics with Poisson initial conditions, including velocity distributions and correlation functions.

Findings

Distributions exhibit stretched-exponential tails with exponents from 1 to infinity.

Characteristic length scale grows as a power law with an exponent between 0 and 0.5.

Full hierarchy of n-point distributions factorizes into two-point conditional probabilities.

Abstract

We derive the statistical properties of one-dimensional Burgers dynamics with stochastic initial conditions for the velocity potential defined by a Poisson point process whose intensity follows a power law with exponent $\alpha > -1$. Working in the inviscid limit and exploiting the geometrical construction of solutions in terms of first-contact parabolas, we derive explicit analytical expressions for a broad set of statistical quantities. These include the one- and two-point probability distributions of the velocity, the multiplicity functions of voids and shocks, and the velocity and density correlation functions together with their associated power spectra. We also show that the full hierarchy of $n$-point distributions factorizes into a sequence of two-point conditional probabilities. This class of initial conditions leads to self-similar evolution and produces probability distributions characterized by stretched-exponential tails, with tail exponents spanning the full range from unity to infinity. The associated characteristic length scale grows as a power law of time, with an exponent lying between zero and one half.