The objective is to demonstrate that a probabilistic cellular automata rule can place reliably a maximal number of dominoes in different active area shapes, exemplarily evaluated for the square and diamond. The basic rule forms domino patterns, but the number of dominoes is not necessarily maximal and the patterns are not always stable. It works with templates derived from domino tiles. The first proposed enhancement (Rule Option 1) can form always stable patterns. The second enhancement (Rule Option 2) can maximize the number of dominoes, but the reached patterns are not always stable. All rules drive the evolution by specific noise injection.
Complex matter may take various forms from granular matter, soft matter, fluid-fluid, or solid-fluid mixtures to compact heterogeneous material. Cellular automata models make a suitable and powerful tool for catching the influence of the microscopic scale in the macroscopic behavior of these complex systems. Rather than a survey, this paper attempts to bring out the main concepts underlying these models. A taxonomy is presented with four general types proposed: sandpile, latticegas, lattice-grain, and hybrid models. A discussion follows with general questions; namely, grain-size, synchronization, topology and scalability, and consistency of the models.
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