Representation of intermediate time-scale motions in stochastic modeling: Analysis on stochastic description of classical Hamiltonian dynamics in relation with measurement imperfection

TL;DR

This paper develops a stochastic modeling framework for intermediate time-scale motions in classical Hamiltonian systems, accounting for measurement errors and showing that complex chaotic systems can be effectively described by Markov processes.

Contribution

It introduces a modified stochastic description for intermediate time-scale motions, incorporating state-dependent noise correlations and cumulants, extending classical white noise models.

Findings

Intermediate time-scale motions require state-dependent noise correlations.

Complex chaotic systems can be modeled as Markov processes with proper deterministic components.

The noise correlation function C(x,p) varies smoothly with slow variables.

Abstract

It is a well established result that, in classical dynamical systems with sufficient time-scale separation, the fast chaotic degrees of freedom are well modeled by (Gaussian) white noise. In this paper, we present the stochastic dynamical description for intermediate time-scale motions with insufficient time-scale separation from the slow dynamical system. First, we analyze how the fast deterministic dynamics can be viewed as stochastic dynamics under experimental observation by intrinsic errors of measurement. Then, we present how the stochastic dynamical description should be modified if intermediate time-scale motions exist: the time correlation of the noise \xi is modified to <\xi(t)\xi(t')> = C(x,p)\delta(t-t'), where C(x,p) is a smooth function of the slow coordinate (x,p), and generally the cumulants of \xi except its average vary as a smooth function of the slow coordinates (x,p). The analysis given in this work actually shows that, regardless of the sufficiency of time-scale separation, any complex (chaotic and ergodic) dynamical system can be well described using Markov process, if we perfectly construct the deterministic part of (extended) stochastic dynamics.