Dynamical response of the Ising model to the time dependent magnetic field with white noise
Ümit Akıncı
Physica A: Statistical Mechanics and its Applications, 2018, vol. 494, issue C, 242-250
Abstract:
The effect of the white noise in time dependent magnetic field on the dynamic behavior of the Ising model has been investigated within the effective field theory based on Glauber type of stochastic process. Discrete white noise has been chosen from both Gaussian and uniform probability distributions. Detailed investigation on probability distribution of dynamical order parameter results that, both type of noise distributions yield the same probability distribution related to the dynamical order parameter, namely Gaussian probability distribution. The variation of the parameters that describe the probability distribution of dynamical order parameter (mean value and standard deviation) with temperature and strength of the noise have been inspected. Also, it has been shown that, rising strength of the noise can induce dynamical phase transition in the system.
Keywords: Dynamical Ising model; White noise; Noisy magnetic field (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437117312840
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:494:y:2018:i:c:p:242-250
DOI: 10.1016/j.physa.2017.12.045
Access Statistics for this article
Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis
More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().