프랙탈(영어: fractal) 이론 : 복잡한 구조는 기적 또는 지적 설계가 아닌 단순한 것이 모여 만들어진다

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프랙탈 이론

프랙탈이란 작은 구조가 전체 구조와 비슷한 형태로 끝없이 되풀이 되는 구조를 말한다.
 
즉, 프랙탈은 부분과 전체가 똑같은 모양을 하고 있다는 "자기 유사성" 개념을 기하학적으로 푼 것으로,
프랙탈은 단순한 구조가 끊임없이 반복되면서 복잡하고 묘한 전체 구조를 만드는 것이다.

프랙탈의 속성은 '자기 유사성(Self-Similarity)'과 '순환성(Recursiveness)'이라는 특징을 가지고 있다.  



자연계의 리아스식 해안선, 동물혈관 분포현태, 나뭇가지 모양, 창문에 성에가 자라는 모습, 산맥의 모습도 다 프랙탈이며
우주의 모든 것이 결국은 프랙탈구조로 되어있다.

프랙탈이라는 말은 IBM의 Thomas J. Watson 연구센터에 근무했던 프랑스 수학자 만델브로트(Benoit B. mandelbrot) 박사가  
1975년 '쪼개다'라는 뜻을 가진 그리스어 '프랙투스(fractus)'에서 따와 처음 만들었다.

만델브로트 박사는 <the fractal geometry of nature>를 출판해 냈는데, 이 책에는 "영국의 해안선 길이가 얼마일까"라는 물음을 던지고 있다.  

리아스식 해안선에는 움푹 들어간 해안선안에 굴곡진 해안선이 계속됐고, 자의 눈금 크기에 따라 전체 해안선의 길이가 달라졌기 때문에 결과적으로 아주 작은 자를 이용하면 해안선의 길이는 무한대로 늘어나게 되는 것이다.


그는 이처럼 같은 모양이 반복되는 구조를 ‘프랙탈’이라고 부르기 시작했다.  


프랙탈(영어: fractal) 또는 프랙털은 일부 작은 조각이 전체와 비슷한 기하학적 형태를 말한다. 

이런 특징을 자기 유사성이라고 하며, 다시 말해 자기 유사성을 갖는 기하학적 구조를 프랙탈 구조라고 한다.

프랙털(Fractal)이라는 용어는 1975년 브누아 망델브로(Benoit Mandelbrot)의 The Fractal Geometry of Nature에서 처음으로 이 단어를 사용하면서 명명되었다. 
 
다만 프랙털의 개념 자체는 이전부터 인지되고 있었다. 
 
예를 들면 카를 바이어슈트라스가 제시한 전구간 미분불능 연속함수는 프랙털의 성질을 보이고 있으며, 더 거슬러 올라가면 야코프 베르누이가 로그함수를 극좌표로 표현하면 자기유사성을 띠는 나선이 됨을 발견한 것이 있다. 어원은 '부서진'이라는 뜻의 라틴어 fractus에서 유래했다. 어원 정보

프랙털 이론은 1975년 망델브로 집합을 연구하면서 시작되었으며, 그 이후로 많은 사례들이 발견되었다. 그 후 자연계가 통계적인 프랙털[2] 모양을 하고 있다는 사실이 밝혀지면서 카오스 이론과 접목시켜서 자연을 모델링 하는데에 굉장히 유용하게 사용된다. 
 
특히 망델브로 집합의 경계면에서는 극도로 미세하게 값이 달라져도 발산하거나 수렴하게 되는데 초기 조건에 극히 민감한 결과를 갖는 시스템이라는 성격에 잘 부합된다.

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고사리의 잎 윤곽이나 나무가 가지를 뻗는 양상, 리아스식 해안선의 모양 등 많은 것들이 자기유사성을 가지고 있다. 

심지어 주식의 변동곡선도 하루 동안의 변화, 한 주 사이의 변화, 한 달, 1년 사이의 변화가 비슷한 형태로 나타나는 자기유사성을 띠고 있다.

이러한 프랙털의 자기복제적인 특징들은 아주 간단한 법칙도 되먹임하면 복잡한 양상을 이끌어낼 수 있음을 보여주고 있다. 
 
이것은 전술한 대로 혼돈 이론을 묘사하는 도구 중 하나일 뿐 아니라, 진화론상의 빈틈을 메꿔줄 도구로 사용될 수도 있다. 

즉, 생물이 나타내는 복잡한 구조가 반드시 기적적인 우연을 필요로 하는 것은 아닐 수 있다는 주장이다.

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What are Fractals?


A fractal is a never-ending pattern. Fractals are infinitely complex patterns that are self-similar across different scales. They are created by repeating a simple process over and over in an ongoing feedback loop. Driven by recursion, fractals are images of dynamic systems – the pictures of Chaos. Geometrically, they exist in between our familiar dimensions. Fractal patterns are extremely familiar, since nature is full of fractals. For instance: trees, rivers, coastlines, mountains, clouds, seashells, hurricanes, etc. Abstract fractals – such as the Mandelbrot Set – can be generated by a computer calculating a simple equation over and over.


What is Chaos Theory?

Chaos is the science of surprises, of the nonlinear and the unpredictable. It teaches us to expect the unexpected. While most traditional science deals with supposedly predictable phenomena like gravity, electricity, or chemical reactions, Chaos Theory deals with nonlinear things that are effectively impossible to predict or control, like turbulence, weather, the stock market, our brain states, and so on. These phenomena are often described by fractal mathematics, which captures the infinite complexity of nature. Many natural objects exhibit fractal properties, including landscapes, clouds, trees, organs, rivers etc, and many of the systems in which we live exhibit complex, chaotic behavior. Recognizing the chaotic, fractal nature of our world can give us new insight, power, and wisdom. For example, by understanding the complex, chaotic dynamics of the atmosphere, a balloon pilot can “steer” a balloon to a desired location. By understanding that our ecosystems, our social systems, and our economic systems are interconnected, we can hope to avoid actions which may end up being detrimental to our long-term well-being.




Principles of Chaos

The Butterfly Effect: This effect grants the power to cause a hurricane in China to a butterfly flapping its wings in New Mexico. It may take a very long time, but the connection is real. If the butterfly had not flapped its wings at just the right point in space/time, the hurricane would not have happened. A more rigorous way to express this is that small changes in the initial conditions lead to drastic changes in the results. Our lives are an ongoing demonstration of this principle. Who knows what the long-term effects of teaching millions of kids about chaos and fractals will be?

Unpredictability: Because we can never know all the initial conditions of a complex system in sufficient (i.e. perfect) detail, we cannot hope to predict the ultimate fate of a complex system. Even slight errors in measuring the state of a system will be amplified dramatically, rendering any prediction useless. Since it is impossible to measure the effects of all the butterflies (etc) in the World, accurate long-range weather prediction will always remain impossible.

Order / Disorder Chaos is not simply disorder. Chaos explores the transitions between order and disorder, which often occur in surprising ways.

Mixing: Turbulence ensures that two adjacent points in a complex system will eventually end up in very different positions after some time has elapsed. Examples: Two neighboring water molecules may end up in different parts of the ocean or even in different oceans. A group of helium balloons that launch together will eventually land in drastically different places. Mixing is thorough because turbulence occurs at all scales. It is also nonlinear: fluids cannot be unmixed.

Feedback: Systems often become chaotic when there is feedback present. A good example is the behavior of the stock market. As the value of a stock rises or falls, people are inclined to buy or sell that stock. This in turn further affects the price of the stock, causing it to rise or fall chaotically.



Fractals: A fractal is a never-ending pattern.

Fractals are infinitely complex patterns that are self-similar across different scales. They are created by repeating a simple process over and over in an ongoing feedback loop. Driven by recursion, fractals are images of dynamic systems – the pictures of Chaos. Geometrically, they exist in between our familiar dimensions. Fractal patterns are extremely familiar, since nature is full of fractals. For instance: trees, rivers, coastlines, mountains, clouds, seashells, hurricanes, etc.

“As far as the laws of mathematics refer to reality, they are not certain, and as far as they are certain, they do not refer to reality.”
-Albert Einstein