Cellular Automata Machines

Abstract

Recently, cellular automata machines with the size, speed, and flexibility for general experimentation at a moderate cost have become available to the scientific community. These machines provide a laboratory in which the ideas presented in this book can be tested and applied to the synthesis of a great variety of systems. Computer scientists and researchers interested in modeling and simulation as well as other scientists who do mathematical modeling will find this introduction to cellular automata and cellular automata machines (CAM) both useful and timely. Cellular automata are the computer scientist's counterpart to the physicist's concept of 'field' They provide natural models for many investigations in physics, combinatorial mathematics, and computer science that deal with systems extended in space and evolving in time according to local laws. A cellular automata machine is a computer optimized for the simulation of cellular automata. Its dedicated architecture allows it to run thousands of times faster than a general-purpose computer of comparable cost programmed to do the same task. In practical terms this permits intensive interactive experimentation and opens up new fields of research in distributed dynamics, including practical applications involving parallel computation and image processing. Contents Introduction • Cellular Automata • The CAM Environment • A Live Demo • The Rules of the Game • Our First rules • Second-order Dynamics • The Laboratory • Neighbors and Neighborhood • Running • Particle Motion • The Margolus Neighborhood • Noisy Neighbors • Display and Analysis • Physical Modeling • Reversibility • Computing Machinery • Hydrodynamics • Statistical Mechanics • Other Applications • Imaging Processing • Rotations • Pattern Recognition • Multiple CAMS • Perspectives and Conclusions Cellular Automata Machines is included in the Scientific Computation Series, edited by Dennis Cannon.

Evolutionary dynamics of RNA-like replicator systems: A bioinformatic approach to the origin of life

“…To these ends, it is important to construct models without explicitly aiming at the production of preconceived output [28]. Instead, the behavior of models are to be explored and discovered [29]: “We want models that talk back to us, models that have a mind of their own” [30, p. 142]. We then compare different models to identify general principles and to determine causal relations.…”

“…In this section, we consider an alternative modeling framework, namely, stochastic cellular automata (CA). CA models also make two implicit assumptions, which contrast with the two mentioned above: replicators interact only locally (i.e., only with those that are spatially close to them); populations are discrete [30, 92]. Using CA as a model, we reconsider the dynamics of hypercycles [93, 94].…”

On Remote and Virtual Experiments in eLearning in Statistical Mechanics and Thermodynamics

“…Implementing a freely programmable cellular automaton [20], VideoEasel is capable of simulating various models of statistical mechanics and related fields.…”

“…Technically, VideoEasel is a client-server architecture with a C++ based number cruncher running on the server that implements a programmable cellular automaton [20], and Java clients that communicate to the server through CORBA [14] middle-ware providing a remote function call interface. As a result, networked access to a laboratory session running on VideoEasel works seamlessly, connecting through the campus wide wireless network of our university.…”

Lattice-gas models of phase separation: interfaces, phase transitions, and multiphase flow

“…Early attempts (Clouqueur and d'Humieres, 1987;Toffoli and Margolus, 1987) led to excitement but not to wide use. A more recent and much awaited machine (Toffoli and Margolus, 1991) has, at the time of this writing, just led to its first prototype; its applicability to scientific computation appears promising, but remains to be exploited.…”