1994Journal of Physics A Mathematical and GeneralRequires access

Boundary conditions and scaling functions of percolation models

Chin‐Kun Hu

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Abstract

We use a histogram Monte Carlo simulation method to calculate the scaling functions of the existence probability E p and the percolation probability P of the site percolation model on square lattices with free and periodic boundary conditions. We find that different boundary conditions give quite different scaling functions near the critical region. However, they give the consistent critical point, critical exponents, and the thermodynamic order parameter from renormalization-group calculations. Similar results are found for other percolation models. The implications of our calculated results for some theoretical problems of current interest are discussed.

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What this paper is about

We use a histogram Monte Carlo simulation method to calculate the scaling functions of the existence probability E p and the percolation probability P of the site percolation model on square lattices with free and periodic boundary conditions. We find that different boundary conditions give quite different scaling functions near the critical region. However, they give the consistent critical point, critical exponents, and the thermodynamic order parameter from renormalization-group calculations. Similar results are found for other percolation models. The implications of our calculated results for some theoretical problems of current interest are discussed.

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Available abstract

We use a histogram Monte Carlo simulation method to calculate the scaling functions of the existence probability E p and the percolation probability P of the site percolation model on square lattices with free and periodic boundary conditions. We find that different boundary conditions give quite different scaling functions near the critical region. However, they give the consistent critical point, critical exponents, and the thermodynamic order parameter from renormalization-group calculations. Similar results are found for other percolation models. The implications of our calculated results for some theoretical problems of current interest are discussed.

Key concepts: Percolation critical exponents, Statistical physics, Scaling, Percolation (cognitive psychology), Directed percolation, Critical exponent, Critical point (mathematics), Monte Carlo method

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