Field:Fractals

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Fractals

A fractal is often defined as a geometry shape that is self-similarity>, or whose magnified parts look like a smaller copy of the whole. It was coined by Benoit Mandelbolt in 1975 from the latin term ‘’fractus’’ meaning ”fragmented” or “irregular”.

This concept can be explained in a commonly used nature analogy involving the coastline of an island:

 Suppose you wanted to measure the total perimeter of an island. You could begin by roughly estimating
 the perimeter of the island by measuring the border of the island from a high vantage point like an
 airplane and using miles as units. Next, to be more accurate, you could walk along the island's borders
 and measure around its various coves and bays using a measuring tape. Then, if you wanted to be really
 accurate, you could carefully measurement around every single protruding rock and detail of the island
 with a yardstick or even a foot-long ruler.

Clearly, the perimeter of the island would grow as you decrease the size of your measuring device and increase the accuracy of your measurements. Also, the island would more or less like similar (in terms of becoming more and more jagged and complex) as you continued to decrease your measuring device.


Contents

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  • Strange attractors

Fractals that are considered strange attractors are generated from a set of functions called attractor maps or systems. These systems are chaotic, because the functions map points in a seemingly random order. These points are actually not completely random and are in fact evolving towards a structure that can eventually be seen as the form of the attractor.
Examples include: Lorenzo Attractor, Henon Attractor, Cantor Dust , and Rossler Attractor.
  • Random fractals

These fractals are created through stochastic methods, meaning there are no set functions that determine the behavior of the fractals. Although the fractals still must have rules that they follow, they are unlike chaotic IFS fractals because chaotic fractals are governed by a set of functions that are picked according to probability and these follow some sort of a pattern (and grow exponentially), while random fractals are simply random (and grow randomly).
Examples include: Levy Flights, Brownian Motion, Brownian Tree, and fractal landscapes.
  • Escape-time (“orbit”) fractals

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  • Strange attractors

Fractals that are considered strange attractors are generated from a set of functions called attractor maps or systems. These systems are chaotic, because the functions map points in a seemingly random order. These points are actually not completely random and are in fact evolving towards a structure that can eventually be seen as the form of the attractor.
Examples include: Lorenzo Attractor, Henon Attractor, Cantor Dust , and Rossler Attractor.
  • Random fractals

These fractals are created through stochastic methods, meaning there are no set functions that determine the behavior of the fractals. Although the fractals still must have rules that they follow, they are unlike chaotic IFS fractals because chaotic fractals are governed by a set of functions that are picked according to probability and these follow some sort of a pattern (and grow exponentially), while random fractals are simply random (and grow randomly).
Examples include: Levy Flights, Brownian Motion, Brownian Tree, and fractal landscapes.
  • Escape-time (“orbit”) fractals

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