Swarm behaviour
A flock of auklets exhibit swarm behaviour From a more abstract point of view, swarm behaviour is the collective motion of a large number of self-propelled entities.[1] From the perspective of the mathematical modeller, it is an emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination. Swarm behaviour was first simulated on a computer in 1986 with the simulation program boids.[2] This program simulates simple agents (boids) that are allowed to move according to a set of basic rules. The model was originally designed to mimic the flocking behaviour of birds, but it can be applied also to schooling fish and other swarming entities. Models[edit]

Cellular automaton
The concept was originally discovered in the 1940s by Stanislaw Ulam and John von Neumann while they were contemporaries at Los Alamos National Laboratory. While studied by some throughout the 1950s and 1960s, it was not until the 1970s and Conway's Game of Life, a two-dimensional cellular automaton, that interest in the subject expanded beyond academia. In the 1980s, Stephen Wolfram engaged in a systematic study of one-dimensional cellular automata, or what he calls elementary cellular automata; his research assistant Matthew Cook showed that one of these rules is Turing-complete. Wolfram published A New Kind of Science in 2002, claiming that cellular automata have applications in many fields of science. These include computer processors and cryptography.

Data structure
Different kinds of data structures are suited to different kinds of applications, and some are highly specialized to specific tasks. For example, B-trees are particularly well-suited for implementation of databases, while compiler implementations usually use hash tables to look up identifiers. Data structures provide a means to manage large amounts of data efficiently, such as large databases and internet indexing services. Usually, efficient data structures are a key to designing efficient algorithms. Some formal design methods and programming languages emphasize data structures, rather than algorithms, as the key organizing factor in software design.
Agent-Based Computational Economics (Tesfatsion)
Growing Economies from the Bottom Up Welcome to the ACE Website Agent-based computational economics (ACE) is the computational modeling of economic processes (including whole economies) as open-ended dynamic systems of interacting agents. Here "agent" refers broadly to a bundle of data and methods representing an entity residing within the dynamic system. Examples of possible agents include: individuals (e.g., consumers and producers); social groupings (e.g., families, firms, communities, and government agencies); institutions (e.g., markets and regulatory systems); biological entities (e.g., crops, livestock, and forests); and physical entities (e.g., infrastructure, weather, and geographical regions). Thus, agents can range from passive system features to active data-gathering decision makers capable of sophisticated learning and social behaviors.

Euler equations (fluid dynamics)
In fluid dynamics, the Euler equations are a set of equations governing inviscid flow. They are named after Leonhard Euler. The equations represent conservation of mass (continuity), momentum, and energy, corresponding to the Navierâ€“Stokes equations with zero viscosity and without heat conduction terms.
Fractal
Figure 1a. The Mandelbrot set illustrates self-similarity. As the image is enlarged, the same pattern re-appears so that it is virtually impossible to determine the scale being examined.

Category:Data structures
In computer science, a data structure is a way of storing data in a computer so that it can be used efficiently. Often a carefully chosen data structure will allow a more efficient algorithm to be used. The choice of the data structure must begin from the choice of an abstract data structure. Subcategories This category has the following 13 subcategories, out of 13 total.
Lagrangian
The Lagrangian, L, of a dynamical system is a function that summarizes the dynamics of the system. The Lagrangian is named after Italian-French mathematician and astronomer Joseph Louis Lagrange. The concept of a Lagrangian was introduced in a reformulation of classical mechanics introduced by Lagrange known as Lagrangian mechanics.

Percolation threshold
Percolation threshold is a mathematical term related to percolation theory , which is the formation of long-range connectivity in random systems. Below the threshold a giant connected component does not exist; while above it, there exists a giant component of the order of system size. In engineering and coffee making , percolation represents the flow of fluids through porous media, but in the mathematics and physics worlds it generally refers to simplified lattice models of random systems or networks (graphs), and the nature of the connectivity in them. The percolation threshold is the critical value of the occupation probability p , or more generally a critical surface for a group of parameters p 1 , p 2 , ..., such that infinite connectivity ( percolation ) first occurs.

List of data structures
List of data structures From Wikipedia, the free encyclopedia Jump to: navigation, search This is a list of data structures.
Swarm robotics
Swarm of open-source Jasmine micro-robots recharging themselves Swarm robotics is a new approach to the coordination of multirobot systems which consist of large numbers of mostly simple physical robots. It is supposed that a desired collective behavior emerges from the interactions between the robots and interactions of robots with the environment. This approach emerged on the field of artificial swarm intelligence, as well as the biological studies of insects, ants and other fields in nature, where swarm behaviour occurs.