Monte Carlo Simulation of the Non-universal Short-Time Behavior for a Kinetic 2-D Ising Model

2000 ◽  
Vol 33 (2) ◽  
pp. 205-208 ◽  
Author(s):  
Ye AiJun ◽  
Pan ZhiGang ◽  
Chen Yuan ◽  
Li ZhiBing
Author(s):  
Subir K Das ◽  
Nalina Vadakkayil

For quicker formation of ice, before inserting inside a refrigerator, heating up of a body of water can be beneficial. We report first observation of a counterpart of this intriguing...


2005 ◽  
Vol 16 (04) ◽  
pp. 585-589 ◽  
Author(s):  
MUNEER A. SUMOUR ◽  
M. M. SHABAT

The existence of spontaneous magnetization of Ising spins on directed Barabasi–Albert networks is investigated with seven neighbors, by using Monte Carlo simulations. In large systems, we see the magnetization for different temperatures T to decay after a characteristic time τ(T), which is extrapolated to diverge at zero temperature.


2021 ◽  
Vol 574 ◽  
pp. 125973
Author(s):  
K.P. do Nascimento ◽  
L.C. de Souza ◽  
A.J.F. de Souza ◽  
André L.M. Vilela ◽  
H. Eugene Stanley

2001 ◽  
Vol 15 (25) ◽  
pp. 1141-1146 ◽  
Author(s):  
T. TOMÉ ◽  
C. S. SIMÕES ◽  
J. R. DRUGOWICH DE FELÍCIO

We study the short time dynamics of a two-dimensional Ising model with a line of defects. The dynamical critical exponent θ associated to the early time regime at the critical temperature was obtained by Monte Carlo simulations. The exponent θ was estimated by a method where the quantity of interest is the time correlation of the magnetization.


1994 ◽  
Vol 05 (03) ◽  
pp. 547-560 ◽  
Author(s):  
P.D. CODDINGTON

Monte Carlo simulation is one of the main applications involving the use of random number generators. It is also one of the best methods of testing the randomness properties of such generators, by comparing results of simulations using different generators with each other, or with analytic results. Here we compare the performance of some popular random number generators by high precision Monte Carlo simulation of the 2-d Ising model, for which exact results are known, using the Metropolis, Swendsen-Wang, and Wolff Monte Carlo algorithms. Many widely used generators that perform well in standard statistical tests are shown to fail these Monte Carlo tests.


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