Application of Local Information Entropy in Cluster Monte Carlo Algorithms
Authors:
- Artur Chrobak,
- Grzegorz Sebastian Ziółkowski,
- Dariusz Chrobak
Abstract
The chapter refers to a modification of the so-called adding probability used in cluster Monte Carlo algorithms. The modification is based on the fact that in real systems, different properties can influence its clusterization. Finally, an additional factor related to property disorder was introduced into the adding probability, which leads to more effective free energy minimization during MC iteration. As a measure of the disorder, we proposed to use a local information entropy. The proposed approach was tested and compared with the classical methods, showing its high efficiency in simulations of multiphase magnetic systems where magnetic anisotropy was used as the property influencing the system clusterization.
- Record ID
- USL94bb698e0bd94b9eb400111bd209015b
- Author
- Pages
- 2-17
- Publication size in sheets
- 0.80
- Book
- Theory, Application, and Implementation of Monte Carlo Method in Science and Technology, 2019, London, Intech, ISBN 978-1-78985-545-6
- Keywords in English
- Monte Carlo simulations, Cluster Monte Carlo methods
- DOI
- DOI:10.5772/intechopen.88627 Opening in a new tab
- Handle.net URL
- hdl.handle.net/20.500.12128/12331 Opening in a new tab
- Language
- eng (en) English
- License
- File
-
- File: 1
- Application of Local Information Entropy in Cluster Monte Carlo Algorithms, File Chrobak_Application_of_Local_Information_Entropy_in_Cluster_Monte_Carlo_Algorithms.pdf / 6 MB
- Chrobak_Application_of_Local_Information_Entropy_in_Cluster_Monte_Carlo_Algorithms.pdf
- publication date: 06-02-2024
- Application of Local Information Entropy in Cluster Monte Carlo Algorithms, File Chrobak_Application_of_Local_Information_Entropy_in_Cluster_Monte_Carlo_Algorithms.pdf / 6 MB
-
- Score (nominal)
- 5
- Score source
- BIBLIOGRAFIA DOROBKU PRACOWNIKÓW UŚ
- Publication indicators
- = 0
- Uniform Resource Identifier
- https://opus.us.edu.pl/info/article/USL94bb698e0bd94b9eb400111bd209015b/
- URN
urn:uni-kat-prod:USL94bb698e0bd94b9eb400111bd209015b
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.