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Self-organization and complexity: a new age for theory, computation and experiment
1Centre for Computational Science, Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK. p.v.coveney@ucl.ac.uk
Summary
This study explores self-organization in complex systems, focusing on emergent properties from simple microscopic dynamics. Computational grids are poised to advance the study of these fascinating nonlinear dissipative dynamical systems.
Area of Science:
- Physics
- Physical Chemistry
- Complex Systems
Background:
- Self-organization is a key property of nonlinear dissipative dynamical systems operating far from equilibrium.
- Complex systems often exhibit emergent macroscopic properties from simple microscopic dynamics.
Purpose of the Study:
- To analyze the emergent complexity in self-organizing systems.
- To investigate physical and physicochemical examples exhibiting self-organization.
- To discuss the impact of computational grids on studying complex systems.
Main Methods:
- Analysis of mesoscopic models in fluid dynamics.
- Application of modern approaches to nucleation and growth phenomena.
- Conceptual framework for understanding emergent complexity.
Main Results:
- Demonstrated self-organization in fluid dynamics and nucleation/growth models.
- Highlighted the emergence of complex macroscopic properties from simple microscopic rules.
- Identified computational grids as a significant tool for future research.
Conclusions:
- Self-organization is a fundamental concept in far-from-equilibrium systems.
- Understanding emergent complexity requires analyzing systems with simple underlying dynamics.
- Computational advancements will accelerate the study of complex self-organizing phenomena.