Book contents
- Frontmatter
- Contents
- Preface
- 1 Basic concepts of dynamical systems theory
- 2 Dynamical indicators for chaotic systems: Lyapunov exponents, entropies and beyond
- 3 Coarse graining, entropies and Lyapunov exponents at work
- 4 Foundation of statistical mechanics and dynamical systems
- 5 On the origin of irreversibility
- 6 The role of chaos in non-equilibrium statistical mechanics
- 7 Coarse-graining equations in complex systems
- 8 Renormalization-group approaches
- Index
7 - Coarse-graining equations in complex systems
Published online by Cambridge University Press: 19 October 2009
- Frontmatter
- Contents
- Preface
- 1 Basic concepts of dynamical systems theory
- 2 Dynamical indicators for chaotic systems: Lyapunov exponents, entropies and beyond
- 3 Coarse graining, entropies and Lyapunov exponents at work
- 4 Foundation of statistical mechanics and dynamical systems
- 5 On the origin of irreversibility
- 6 The role of chaos in non-equilibrium statistical mechanics
- 7 Coarse-graining equations in complex systems
- 8 Renormalization-group approaches
- Index
Summary
To develop the skill of correct thinking is in the first place to learn what you have to disregard. In order to go on, you have to know what to leave out: this is the essence of effective thinking.
Kurt GödelAlmost all the interesting dynamic problems in science and engineering are characterized by the presence of more than one significant scale, i.e. there is a variety of degrees of freedom with very different time scales. Among numerous important examples we can mention protein folding and climate. While the time scale of vibration of covalent bonds is O(10–15 s), the folding time for proteins may be of the order of seconds. Also in the case of climate, the characteristic times of the involved processes vary from days (for the atmosphere) to O(103 yr) (for the deep ocean and ice shields). In such a situation one says that the system has a multiscale character (E and Engquist 2003).
The necessity of treating the “slow dynamics” in terms of effective equations is both practical (even modern supercomputers are not able to simulate all the relevant scales involved in certain difficult problems) and conceptual: effective equations are able to catch some general features and to reveal dominant ingredients which can remain hidden in the detailed description. The study of multiscale problems has a long history in science (in particular in mathematics): an early important example is the averaging method in mechanics (Arnold 1976).
- Type
- Chapter
- Information
- Chaos and Coarse Graining in Statistical Mechanics , pp. 185 - 216Publisher: Cambridge University PressPrint publication year: 2008