Lorenz system in the context of "Dynamical systems"

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⭐ Core Definition: Lorenz system

The Lorenz system is a three-dimensional classical dynamic system represented by three ordinary differential equations . It was first developed by the meteorologist Edward Lorenz and describes chaotic behavior of fluid movement when subjected to heating.

Although the Lorenz system is deterministic, its dynamics depend on the choice of initial parameters. For some ranges of parameters, the system is predictable as trajectories settle into fixed points or simple periodic orbits. In contrast, for other parameter ranges, the system becomes chaotic and the solutions never settle down but instead trace out the butterfly-shaped Lorenz attractor, popularly known as butterfly effect. In this regime, small differences in initial conditions grows exponentially making long-term prediction practically impossible.

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👉 Lorenz system in the context of Dynamical systems

In mathematics, a dynamical system is a system in which a function describes the time dependence of a point in an ambient space, such as in a parametric curve. Examples include the mathematical models that describe the swinging of a clock pendulum, the flow of water in a pipe, the random motion of particles in the air, and the number of fish each springtime in a lake. The most general definition unifies several concepts in mathematics such as ordinary differential equations and ergodic theory by allowing different choices of the space and how time is measured. Time can be measured by integers, by real or complex numbers or can be a more general algebraic object, losing the memory of its physical origin, and the space may be a manifold or simply a set, without the need of a smooth space-time structure defined on it.

At any given time, a dynamical system has a state representing a point in an appropriate state space. This state is often given by a tuple of real numbers or by a vector in a geometrical manifold. The evolution rule of the dynamical system is a function that describes what future states follow from the current state. Often the function is deterministic, that is, for a given time interval only one future state follows from the current state. However, some systems are stochastic, in that random events also affect the evolution of the state variables.

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