Ads
related to: math pattern generator
Search results
Results From The WOW.Com Content Network
Rule 30: Wolfram's Pseudo-random Bit Generator. Recipe 32 at David Griffeath's Primordial Soup Kitchen. Repeating Rule 30 patterns. A list of patterns that, when repeated to fill the cells of a Rule 30 automaton, repeat themselves after finitely many time steps. Frans Faase, 2003. Archived from the Original on 2013-08-08; Paving Mosaic Fractal ...
Three examples of Turing patterns Six stable states from Turing equations, the last one forms Turing patterns. The Turing pattern is a concept introduced by English mathematician Alan Turing in a 1952 paper titled "The Chemical Basis of Morphogenesis" which describes how patterns in nature, such as stripes and spots, can arise naturally and autonomously from a homogeneous, uniform state.
In mathematics and physics, the term generator or generating set may refer to any of a number of related concepts. The underlying concept in each case is that of a smaller set of objects, together with a set of operations that can be applied to it, that result in the creation of a larger collection of objects, called the generated set .
The fern is one of the basic examples of self-similar sets, i.e. it is a mathematically generated pattern that can be reproducible at any magnification or reduction. Like the Sierpinski triangle , the Barnsley fern shows how graphically beautiful structures can be built from repetitive uses of mathematical formulas with computers.
This version of the pea pattern eventually forms a cycle with the two "atomic" terms 23322114 and 32232114. Other versions of the pea pattern are also possible; for example, instead of reading the digits as they first appear, one could read them in ascending order instead (sequence A005151 in the OEIS). In this case, the term following 21 would ...
It can be shown that if is a pseudo-random number generator for the uniform distribution on (,) and if is the CDF of some given probability distribution , then is a pseudo-random number generator for , where : (,) is the percentile of , i.e. ():= {: ()}. Intuitively, an arbitrary distribution can be simulated from a simulation of the standard ...