Stochastic Diagrams for Critical Point Spectra

TL;DR

This paper introduces a non-perturbative stochastic diagram technique for analyzing the spectra of nonlinear stochastic differential equations, especially near critical points, with excellent agreement to simulations.

Contribution

It develops a novel stochastic diagram method for calculating spectra of nonlinear stochastic systems without perturbation, applicable to spatially extended problems.

Findings

Accurate spectra near critical points

Effective long-time extrapolation methods

Excellent agreement with numerical simulations

Abstract

A new technique for calculating the time-evolution, correlations and steady state spectra for nonlinear stochastic differential equations is presented. To illustrate the method, we consider examples involving cubic nonlinearities in an N-dimensional phase-space. These serve as a useful paradigm for describing critical point phase transitions in numerous equilibrium and non-equilibrium systems. The technique presented here is not perturbative. It consists in developing the stochastic variable as a power series in time, and using this to compute the short time expansion for the correlation functions. This, in turn, is extrapolated to large times and Fourier transformed to obtain the spectrum. A stochastic diagram technique is developed to facilitate computation of the coefficients of the relevant power series expansion. Two different types of long-time extrapolation technique, involving either simple exponentials or logarithmic rational approximations, are evaluated for third-order diagrams. The analytical results thus obtained are compared with numerical simulations, together with exact results available in special cases. The agreement is excellent in the neighborhood of the critical point. Finally, we emphasize that the technique is also applicable to more general stochastic problems involving spatial variation in addition to temporal variation.