Nonlinear Sciences – Cellular Automata and Lattice Gases
Scientific paper
2008-04-09
Alejandro Tejedor et al 2008 J. Phys. A: Math. Theor. 41 375102 (16pp)
Nonlinear Sciences
Cellular Automata and Lattice Gases
A note has been added in which it is discussed the possible application of the model to describe some properties of the dynami
Scientific paper
10.1088/1751-8113/41/37/375102
A simple one-dimensional cellular automaton model with threshold dynamics is introduced. The cumulative distribution of the size of the relaxations is analytically computed and behaves as a power law with an exponent equal to -1. This coincides with the phenomenological Gutenberg-Richter behavior observed in Seismology for the cumulative statistics of earthquakes at the regional or global scale. The key point of the model is the zero-load state of the system after the occurrence of any relaxation, no matter what its size. This leads to an equipartition of probability between all possible load configurations in the system during the successive loading cycles. Each cycle ends with the occurrence of the greatest -or characteristic- relaxation in the system. The duration of the cycles in the model is statistically distributed with a coefficient of variation ranging from 0.5 to 1. The predictability of the characteristic relaxations is evaluated by means of error diagrams. This model illustrates the value of taking into account the refractory periods to obtain a considerable gain in the quality of the predictions.
Ambroj Samuel
Gomez Javier B.
Pacheco Amalio. F.
Tejedor Alejandro
No associations
LandOfFree
Predictability of the large relaxations in a cellular automaton model does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Predictability of the large relaxations in a cellular automaton model, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Predictability of the large relaxations in a cellular automaton model will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-574976