F r a c t a l s E v e r y w h e r e - University of...
Transcript of F r a c t a l s E v e r y w h e r e - University of...
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Europaisches Forum Alpbach 16 August, 2003
Fractals Everywhere
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Scales in the description of systems
A Scale is the level of detail at which we are looking at a system.
Examples:
• The Economy: Personal finances (Scale is each person) to
Global economy (Scale is the whole world).
• The Physics of the Universe : Subatomic particles to the entire
universe.
• The Human body : Molecules to the complete person.
The laws governing a system are usually different at different scales.
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Self-similarity
A system is Self similar if it looks like itself at different scales.
Leibniz : “A drop of water contained a whole teeming universe,
containing in turn, water drops and new universes within.”
- James Gleick in “Chaos”.
The idea of a Homunculus.
These ideas died in the face of scientific discoveries that each
smaller scale had different and “more fundamental” laws.
Reductionism
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Self Similarity
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Scaling Laws.
Self similarity implies the lack of a preferred scale. Scaling laws
arise when there is no preferred scale.
Physical laws, e.g., gravity, are universal, and are valid independent
of the scale of the objects (classically).
This can be used to show that similar behavior has to occur across
many scales – Dimensional Analysis.
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Vortex streetin the atmosphere
Guadalupe Island,Aug 20, 1999
Image CourtesyNASA Goddard
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Shetland Islands Falkland Islands
Images Courtesy NASA Goddard
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Flow in a Soap Film
Experiments byMaarten Rutgers,
Ohio State University
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Scaling laws II
The behavior of systems that is scale-independent, is governed by
Scaling laws
Y ∼ Xα.
A straight line on a log-log plot.
Slope of the straight line is α.
“Nontrivial” scaling laws also arise in the study of Phase
transitions.
Renormalization and Universality.
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Scaling laws show up in many surprising contexts
• Frequency of the occurrence of words in written text.
• The number of islands of a given size.
• Distribution of times of error free transmission over a
communication link.
• The fluctuations in the price of cotton.
• The frequency of earthquakes a function of their magnitude.
• The bifurcation diagram of the logistic map.
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• Scaling laws in Biology.
• The distribution of stock market crashes.
• The distribution of avalanches in a sandpile.
• Disease epidemics.
• Extinctions.
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From “Evolution” by Carl Zimmer
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Zipf’s Law
p(n) ∼ n−1.
• The frequency of occurrence of words in written text scales
with the rank of the word occurrence.
• The population of cities scales with the ranking of the cities by
population.
• The number of hits per month of websites, scales with their
ranking by poularity.
Lotka’s law – a(n) ∼ n−2, where a(n) is the number of authors
who write n scientific papers.
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Scaling Laws in Biology
From “Why is Animal size so important”Knut Schmidt-Nielsen
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Animal SizeNumber of life species
Size
1-2 mm
5-6 cm
2-3 m
30-40 cm Niches in nature and gravitylimit large size
S/V effect,surface tension, viscosity andBrownain motion limit small size tension and
103-104 µm
104-105 µm
105-106 µm
106-107 µm
> 107 µm
102-103 µm< 102 µm
1
10
100
1000
10000
100000
1000000
Biosensors in the 10 to 100µmrange face evaporation ofliquids
Image courtesy Prof. Marc MadouUniversity of California, Irvine
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Fractals
Benoit Mandelbrot - French mathematician (also economist,
physiologist, ...), later worked at IBM.
The “father” of fractal geometry.
The world around us is not smooth. it possesses structures on all
scales.
All the examples described have events on a whole range of scales.
The Geometry of nature.
Geometric structures on all scales - Fractals.
How long is the coastline of Britain ?
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The Kochcurve
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The Sierpinski Triangle
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The Sierpinski Carpet
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Cantor sets and other “simple” fractals
• Structure on all scales.
• Iteration - Repeated application of a rule.
• The rule is nonlinear.
• Exact Self-similarity.
Questions
• What is the connection to dynamical systems?
• Are fractals just mathematical curiosities or do they occur in
the “real” world?
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The Mandelbrot Set
Complex numbers : a + ib.
i=√−1.
Consider the nonlinear map
zn+1 = z2n + c.
Start at z0 = 0.
Color the point c black if the orbit does not escape to infinity.
In practice, take a large distance and look at the number of steps it
takes for the orbit to get outside that distance. Color accordingly.
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The Journey through the Mandelbrot set
• Structure on all scales.
• There are different kinds of structures at any given scale.
• The structures on different scales are not exactly the same.
• No exact self-similarity.
Statistical self-similarity.
If one is shown a picture of a part of the Mandelbrot set, one can’t
look at it and find the scale (the level of magnification).
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Fractals Everywhere
• Art and Architecture.
• Image processing and storage.
• The Geometry of nature.
• Description of dynamical systems.
• Strange Attractors.
• Many applied sciences including Oil exploration, Ore
extraction, etc.
• How does a cookie crumble?
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A fractal fern
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Frosty the “snowman”. Image courtesy Frank Roussel fractal page.
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A tree. Image courtesy E. Stephen Mack.
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A computer generated Martian landscape. Image
courtesy J. C. Sprott.
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The Lorenz Attractor
Generated by the Program “Chaotic Flows”
Thanks to John Lindner, Bryan Prusha and Josh Bozeday, University of Wooster
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The Ikeda attractor
Image courtesy the University of Maryland
Chaos Group
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Characterization of Fractals
Box Counting Dimension or Capacity Dimension - D0.
Draw the set on a graph paper with squares of size ε.
Count the number of boxes needed to cover the set N(ε).
Make the size of the grid on the graph paper smaller and count the
number of boxes again.
Plot N(ε) as a function of ε on a log-log plot.
One obtains a straight line, i.e., it is a scaling law.
The slope of the line gives the negative of the dimension, so that
N(ε) ∼ ε−D0
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Capacity dimension for various sets
10−6
10−4
10−2
100
e
100
105
1010
1015
1020
N(e
)
Box counting
CurveCantor setAreaKoch curveVolumeSierpinski carpet
This figure shows the capacity dimension of various sets.
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Fractals
A fractal is a set with a box counting dimension that is not an
integer.
First defined by Benoit Mandelbrot.
• For the Cantor set, D0 = 0.6309.
• For the Koch curve, D0 = 1.2619.
• For the Sierpinski carpet, D0 = 1.8928.
• For the Menger sponge, D0 = 2.7712.
For 0 ≤ D0 ≤ 1, Number of points is infinite but length is zero, for
1 ≤ D0 ≤ 2, length is infinite but area is zero and for 2 ≤ D0 ≤ 3,
area is infinite but volume is zero.
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Scaling Range
• A non integer fractal dimension D0 between 1 and 2 measures
the roughness of the curve.
• A curve occurring in nature cannot be rough on every scale all
the way down to zero. There is a cutoff at Atomic scales.
• On the log-log plot, there is a scaling range in which one
obtains a straight line with a slope equal to −D0.
• There is a cutoff both at small scales and at large scales where
the behavior deviates from this straight line.
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• The object looks like a fractal only on scales in the scaling
range.
• On scales much bigger than the object, it looks like a point
D0 = 0 and on very small scales, it looks smooth and has a
finite volume D0 = 3.
• We can have objects which have no scaling range, so that the
fractal dimension depends on the scale we are looking at.
• We can have objects whose fractal dimension changes from one
point to another. Such objects have a spectrum of Pointwise
dimensions.
• Multifractals - Dimension Spectrum.
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Summary
• We are usually interested in describing a system at many scales.
• A system that is the same at different scales is self-similar.
• Systems that are exactly self-similar lack a preferred scale.
• Systems without a preferred scale obey scaling laws.
• Empirically, it is observed that scaling laws describe a wide
variety of phenomena.
• A geometrical object that is self similar is called a fractal. A
fractal has structure on arbitrarily small scales.
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Summary
• Iterations of nonlinear dynamical systems lead to structures on
arbitrarily small scales and hence to fractals.
• We can characterize a fractal by its Box-counting dimension.
• Fractals are usually not exactly self-similar. They are
self-similar in a statistical sense.
• The Box-counting yields the fractal dimension over a scaling
range.
• Often, the box counting dimension does not uniquely
characterize a fractal. Usually, the fractal object has a
spectrum of dimensions.