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Scaling and Renormalization in Statistical Physics
Abstract
This text provides a thoroughly modern graduate-level introduction to the theory of critical behaviour. Beginning with a brief review of phase transitions in simple systems and of mean field theory, the text then goes on to introduce the core ideas of the renormalization group. Following chapters cover phase diagrams, fixed points, cross-over behaviour, finite-size scaling, perturbative renormalization methods, low-dimensional systems, surface critical behaviour, random systems, percolation, polymer statistics, critical dynamics and conformal symmetry. The book closes with an appendix on Gaussian integration, a selected bibliography, and a detailed index. Many problems are included. The emphasis throughout is on providing an elementary and intuitive approach. In particular, the perturbative method introduced leads, among other applications, to a simple derivation of the epsilon expansion in which all the actual calculations (at least to lowest order) reduce to simple counting, avoiding the need for Feynman diagrams.
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Cites
Wu, F. Y. (1982) · Reviews of Modern Physics · doi:10.1103/revmodphys.54.235 · Cited by 3,570
The Theory of Critical Phenomena: An Introduction to the Renormalization Group
Binney, J. J., Dowrick, N. J., Fisher, A. J., & Newman, M. E. J. (1992) · doi:10.1093/oso/9780198513940.001.0001 · Cited by 1,243
Cited by
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Metadata
- Authors
- Cardy, J.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 1996-04-26
- OpenAlex
- W3174658623
- Cited by
- 1,902
- References
- 2
- FWCI
- 4.36
- Type
- book
- Language
- en
- Open access
- closed
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- Cardy, J. (1996). Scaling and Renormalization in Statistical Physics. Cambridge University Press eBooks. https://doi.org/10.1017/cbo9781316036440
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.