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Extracting macroscopic dynamics: model problems and algorithms

REVIEW ARTICLE

Dror Givon1, Raz Kupferman1 and Andrew Stuart2

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INVITED ARTICLE

Recommended by R Krasny

In many applications, the primary objective of numerical simulation of time-evolving systems is the prediction of coarse-grained, or macroscopic, quantities. The purpose of this review is twofold: first, to describe a number of simple model systems where the coarse-grained or macroscopic behaviour of a system can be explicitly determined from the full, or microscopic, description; and second, to overview some of the emerging algorithmic approaches that have been introduced to extract effective, lower-dimensional, macroscopic dynamics.

The model problems we describe may be either stochastic or deterministic in both their microscopic and macroscopic behaviour, leading to four possibilities in the transition from microscopic to macroscopic descriptions. Model problems are given which illustrate all four situations, and mathematical tools for their study are introduced. These model problems are useful in the evaluation of algorithms. We use specific instances of the model problems to illustrate these algorithms. As the subject of algorithm development and analysis is, in many cases, in its infancy, the primary purpose here is to attempt to unify some of the emerging ideas so that individuals new to the field have a structured access to the literature. Furthermore, by discussing the algorithms in the context of the model problems, a platform for understanding existing algorithms and developing new ones is built.


PACS

02.60.Cb Numerical simulation; solution of equations

05.45.-a Nonlinear dynamics and nonlinear dynamical systems

02.60.Gf Algorithms for functional approximation

05.10.-a Computational methods in statistical physics and nonlinear dynamics

MSC

82-08 Computational methods

65Cxx Probabilistic methods, simulation and stochastic differential equations (For theoretical aspects, see 68U20 and 60H35)

65D15 Algorithms for functional approximation

37M05 Simulation

Subjects

Computational physics

Statistical physics and nonlinear systems

Dates

Issue 6 (November 2004)

Received 5 June 2003, in final form 7 April 2004

Published 20 August 2004



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