A Multiscale Cellular Automata Model for Simulating Complex Transportation Systems Artificial Neural Networks Systems Based on DFT Parameters and Molecular Field Analysis - Computational Tools for Prediction of Ethylbenzene Dehydrogenase Reaction Kinetics

We present a new two-level numerical model describing the evolution of transportation network. Two separate but mutually interacting sub-systems are investigated: a starving environment and the network. We assume that the slow modes of the environment growth can be modeled with classical cellular automata (CA) approach. The fast modes representing the transportation network, we approximate by the graph of cellular automata (GCA). This allows the simulation of transportation systems over larger spatio-temporal scales and scrutinizing global interactions between the network and the environment. We show that the model can mimic the realistic evolution of complex river systems. We also demonstrate how the model can simulate a reverse situation. We conclude that the paradigm of this model can be extended further to a general framework, approximating many realistic multiscale transportation systems in diverse fields such as geology, biology and medicine.

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