Book contents
- A Guide to Monte Carlo Simulations in Statistical Physics
- A Guide to Monte Carlo Simulations in Statistical Physics
- Copyright page
- Contents
- Preface
- 1 Introduction
- 2 Some necessary background
- 3 Simple sampling Monte Carlo methods
- 4 Importance sampling Monte Carlo methods
- 5 More on importance sampling Monte Carlo methods for lattice systems
- 6 Off-lattice models
- 7 Reweighting methods
- 8 Quantum Monte Carlo methods
- 9 Monte Carlo renormalization group methods
- 10 Non-equilibrium and irreversible processes
- 11 Lattice gauge models: a brief introduction
- 12 A brief review of other methods of computer simulation
- 13 Monte Carlo simulations at the periphery of physics and beyond
- 14 Monte Carlo studies of biological molecules
- 15 Emerging trends
- Index
- References
9 - Monte Carlo renormalization group methods
Published online by Cambridge University Press: 24 November 2021
- A Guide to Monte Carlo Simulations in Statistical Physics
- A Guide to Monte Carlo Simulations in Statistical Physics
- Copyright page
- Contents
- Preface
- 1 Introduction
- 2 Some necessary background
- 3 Simple sampling Monte Carlo methods
- 4 Importance sampling Monte Carlo methods
- 5 More on importance sampling Monte Carlo methods for lattice systems
- 6 Off-lattice models
- 7 Reweighting methods
- 8 Quantum Monte Carlo methods
- 9 Monte Carlo renormalization group methods
- 10 Non-equilibrium and irreversible processes
- 11 Lattice gauge models: a brief introduction
- 12 A brief review of other methods of computer simulation
- 13 Monte Carlo simulations at the periphery of physics and beyond
- 14 Monte Carlo studies of biological molecules
- 15 Emerging trends
- Index
- References
Summary
The concepts of scaling and universality presented in Chapter 2 can be given a concrete foundation through the use of renormalization group (RG) theory. The fundamental physical ideas underlying RG theory were introduced by Kadanoff (1971) in terms of a simple coarse-graining approach, and a mathematical basis for this viewpoint was completed by Wilson (1971). Kadanoff divided the system up into cells of characteristic size ba, where a is the nearest neighbor spacing, and ba < ξ , where ξ is the correlation length of the system (see Fig. 9.1). The singular part of the free energy of the system can then be expressed in terms of cell variables instead of the original site variables, i.e.
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- A Guide to Monte Carlo Simulations in Statistical Physics , pp. 416 - 429Publisher: Cambridge University PressPrint publication year: 2021