Abstract
This paper describes the deep knowledge encoded into sets of objects so that their aggregated behaviour simulates the dynamics of some physical systems. The details of the mathematical models used to date and their application to a number of standard numerical experiments are presented. A multi-time step scheme is given for cases where different time steps have to be used in different parts of a discretized model. Poincaré sections of the chaotic response of a multi-degree of freedom system indicates that complex large scale simulation can be carried out in this way. The natural parallelism of the method ensures good performance on concurrent architectures. Implementations on snared memory, distributed memory machines based on transputers, and the fine grained connection machine are described.
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