Recently, computational simulation has become a third approach-along with theory and laboratory simulation-to studying and solving scientific problems. In this approach, a computer equipped with problem-solving software tools may represent a virtual laboratory in which researchers can build a model for a given problem and run it under varying conditions. These increasingly complex computational methodologies require sophisticated models and techniques, and vice versa. The authors explain how developing and validating complex models will increasingly depend on significant advances in experimental and testing techniques. High-performance parallel computers gave researchers the ability to implement inherently parallel techniques such as cellular automata (CA), neural networks, and genetic algorithms-significant new mathematical models for describing complex scientific phenomena.
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