Cellular automata modelling and spreadability

Yacoubi, Samira El; Jai, A. El

doi:10.1016/s0895-7177(02)00259-5

Cited by 50 publications

(16 citation statements)

References 13 publications

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“…The ability of CAs to generate a rich spectrum of sometimes complex spatio-temporal patterns from relatively simple underlying transition functions has led to their successful employment in the study of several (a)biological processes [45][46][47][48][49][50]. Models based on CAs can be seen as an alternative to PDE-based models, to provide researchers with a wider range of modeling tools and, in some complex cases, a solution to problems encountered with some of the more classical modeling methods [51,52].…”

Section: Cellular Automatamentioning

confidence: 99%

“…On the other hand, a macroscopic description with classical kinetic equations, normally based on a system of PDEs, poses the problem that in most cases analytical solutions of the resulting equations are not at hand. For that reason, some kind of discretization has to be used to be able to solve the problem numerically, which unavoidably gives rise to approximation errors and stability problems [51,52]. Finally, the macroscopic modeling of electrochemical reactions is unable to capture the stochasticity of the corrosion processes causing that some of the information, important to engineers, is not readily available [42].…”

Section: Choice Of An Appropriate Modelmentioning

confidence: 99%

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Modeling pitting corrosion by means of a 3D discrete stochastic model

et al. 2014

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Section: Cellular Automatamentioning

confidence: 99%

Section: Choice Of An Appropriate Modelmentioning

confidence: 99%

Modeling pitting corrosion by means of a 3D discrete stochastic model

et al. 2014

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“…The Moore neighbourhood contains, in addition, second nearest neighbours northeast, northwest, southeast and southwest that is a total of nine cells. CA have sufficient expressive dynamics to represent phenomena of arbitrary complexity (Wolfram 1986;El Yacoubi and El Jai 2002;Mingarelli 2006), and at the same time can be simulated exactly by digital computers, because of their intrinsic discreteness, i.e. the topology of the simulated object is reproduced in the simulating device (Vichniac 1984).…”

Section: Cellular Automatamentioning

confidence: 99%

On-chip earthquake simulation model using potentials

2008

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A two-dimensional (2-D) Cellular Automata (CA) dynamic system constituted of cells-charges has been proposed for the simulation of the earthquake process. The CA model has been calibrated with the use of real data. The calibration incorporates major seismic characteristics of the area under test. The simulation results are found in good quantitative and qualitative agreement with the recorded Gutenberg-Richter (GR) scaling relations. The model is enriched with a powerful multi-parameter interface that enables the user to load real data from different regions. This paper examines the on-chip realisation of the model and its instrumentation. The CA model hardware implementation is based on Field Programmable Gate Array (FPGA) logic. It utilises an array of 32 9 32 cells. Parameters that construct the local CA rule constitute the input data. The initial seed, which to some extent corresponds to the seismic features of the area under test, is loaded in a semi-parallel way and the process is completed in a certain number of time steps. The automatic response of the processor provides the corresponding GR scaling law of the area under study. The hardware implementation of the CA-based earthquake simulation model is advantageous in terms of low-cost, high-speed, compactness and portability features. It can operate as a preliminary data-acquisition filter that accelerates the evaluation of recorded data as far as its origin time, spatial and magnitude completeness and quality are concerned. Software that performs reliable automatic phase picking, as well as data elaboration, can be assembled next to the earthquake recording instruments (the whole network) output to assure a quick and reliable iteration of the focal parameters of a recorded earthquake (epicentre coordinates, focal depth and magnitude). The dedicated processor can be accommodated right after this stage (before any manual elaboration) focusing on the near real-time development of a reliable qualitative dynamical seismic record and a mapping of the seismic characteristics of the area.

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“…In this paper, we are going to focus on the first strategy which is usually associated to (CA) in software applications [10,11,12]. The (CA) were first introduced by Von Neumann in 1966 to capture essential features of complex systems, where global behaviour arises from the collective effect of simple components locally interacting [13,14]. Usually, the (CA) implements descriptive laws that rule local processes in transition functions.…”

Section: Introductionmentioning

confidence: 99%

Randomizing activity in fire spreading simulations

Innocenti

Cancellieri²

2013

ITM Web of Conferences

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Abstract. In this paper, we suggest the use of activity paradigm to model the uncertain and imprecise nature of the local dynamics in fire spreading simulations. Formerly based on a very simple set of rules, Cellular Automata (CA) models are remarkable in their ability to easily simulate fire spreading behaviour. A new algorithm to track activity concept in a (CA) model is presented. It is called random-activity and it combines activity and stochastic precepts. This algorithm enhances the realism of the spatial simulations. It resolves the problem of the spurious symmetries of the square lattice usually encountered which affect negatively outputs. Experiment shows that the approach enhances fire spreading simulations which are based on local description of the fire dynamics with general descriptive laws. A comparison between classical and random-activity algorithms is performed. The random-activity algorithm proposed outperforms the classical algorithm in terms of graphical restitutions' realism. It might be used to develop fire riskmanagement tools. A new way is presented to combine activity concept and stochastic modelling in a (CA) model.

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Cellular automata modelling and spreadability

Cited by 50 publications

References 13 publications

Modeling pitting corrosion by means of a 3D discrete stochastic model

Modeling pitting corrosion by means of a 3D discrete stochastic model

On-chip earthquake simulation model using potentials

Randomizing activity in fire spreading simulations

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