Burgers dynamics for Poisson point process initial conditions of the Weibull class
Year: 2026
Type: article
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 α > − 1 . This choice realizes a broad and analytically tractable family of strongly non-Gaussian initial states, relevant as limiting cases for problems ranging from turbulence phenomenology to simplified models of structure formation in cosmology. 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.
Author: Patrick Valageas
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,290
OpenAlex ID: W4417292574