Far-from-Equilibrium Time Evolution between two Gamma Distributions

TL;DR

This paper investigates the time evolution of probability distributions in a stochastic logistic model with gamma stationary distributions, revealing how noise influences the transition from stationary to non-stationary states and analyzing the system's informational properties.

Contribution

It introduces a detailed numerical analysis of transient PDFs in a stochastic logistic model, highlighting noise-induced transitions and the role of additive noise in stabilizing solutions.

Findings

Increased noise causes PDFs to deviate from gamma distributions.

Strong noise leads to non-stationary PDFs concentrated at the origin.

Small additive noise restores stationary solutions.

Abstract

Many systems in nature and laboratories are far from equilibrium and exhibit significant fluctuations, invalidating the key assumptions of small fluctuations and short memory time in or near equilibrium. A full knowledge of Probability Distribution Functions (PDFs), especially time-dependent PDFs, becomes essential in understanding far-from-equilibrium processes. We consider a stochastic logistic model with multiplicative noise, which has gamma distributions as stationary PDFs. We numerically solve the transient relaxation problem, and show that as the strength of the stochastic noise increases the time-dependent PDFs increasingly deviate from gamma distributions. For sufficiently strong noise a transition occurs whereby the PDF never reaches a stationary state, but instead forms a peak that becomes ever more narrowly concentrated at the origin. The addition of an arbitrarily small amount of additive noise regularizes these solutions, and re-establishes the existence of stationary solutions. In addition to diagnostic quantities such as mean value, standard deviation, skewness and kurtosis, the transitions between different solutions are analyzed in terms of entropy and information length, the total number of statistically distinguishable states that a system passes through in time.