Monte-Carlo simulation on a 2-D random point pattern : ising model and its application to econophysics

2002 ◽  
Author(s):  
Chi-Wah Kong
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ising Model ◽  
Random Point ◽  
Point Pattern

Should a hotter paramagnet transform quicker to a ferromagnet? Monte Carlo simulation results for Ising model

2021 ◽  
Author(s):  
Subir K Das ◽  
Nalina Vadakkayil
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ising Model ◽  
Simulation Results ◽  
Heating Up

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...

scholarly journals MONTE CARLO SIMULATION OF ISING MODEL ON DIRECTED BARABASI–ALBERT NETWORK

2005 ◽  
Vol 16 (04) ◽  
pp. 585-589 ◽  
Author(s):  
MUNEER A. SUMOUR ◽  
M. M. SHABAT
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ising Model ◽  
Monte Carlo Simulations ◽  
Characteristic Time ◽  
Spontaneous Magnetization ◽  
Zero Temperature ◽  
Different Temperatures ◽  
Large Systems ◽  
Ising Spins

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.

scholarly journals ANALYSIS OF RANDOM NUMBER GENERATORS USING MONTE CARLO SIMULATION

1994 ◽  
Vol 05 (03) ◽  
pp. 547-560 ◽  
Author(s):  
P.D. CODDINGTON
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Ising Model ◽  
High Precision ◽  
Random Number ◽  
Statistical Tests ◽  
Exact Results ◽  
Random Number Generators ◽  
Monte Carlo Algorithms

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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