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Nonlinear Science FAQ

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This is version 2.0 (Sept. 2003) of the Frequently Asked Questions document
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[1.1] What's New?

      Fixed lots of broken and outdated links. A few sites seem to be gone, 
and some new sites appeared.

   To some extent this FAQ is now been superseded by the Dynamical Systems site 
run by SIAM. See There you will find a 
glossary that contains most of the answers in this FAQ plus new ones. There is 
also a growing software list. You are encouraged to contribute to this list, 
and can do so interactively.

[1]   About Sci.nonlinear FAQ
[1.1] What's New?
[2]   Basic Theory
[2.1] What is nonlinear?
[2.2] What is nonlinear science?
[2.3] What is a dynamical system?
[2.4] What is phase space?
[2.5] What is a degree of freedom?
[2.6] What is a map?
[2.7] How are maps related to flows (differential equations)?
[2.8] What is an attractor?
[2.9] What is chaos?
[2.10] What is sensitive dependence on initial conditions?
[2.11] What are Lyapunov exponents?
[2.12] What is a Strange Attractor?
[2.13] Can computers simulate chaos?
[2.14] What is generic?
[2.15] What is the minimum phase space dimension for chaos?
[3]   Applications and Advanced Theory
[3.1] What are complex systems?
[3.2] What are fractals?
[3.3] What do fractals have to do with chaos?
[3.4] What are topological and fractal dimension?
[3.5] What is a Cantor set?
[3.6] What is quantum chaos?
[3.7] How do I know if my data are deterministic?
[3.8] What is the control of chaos?
[3.9] How can I build a chaotic circuit?
[3.10] What are simple experiments to demonstrate chaos?
[3.11] What is targeting?
[3.12] What is time series analysis?
[3.13] Is there chaos in the stock market?
[3.14] What are solitons?
[3.15] What is spatio-temporal chaos?
[3.16] What are cellular automata?
[3.17] What is a Bifurcation?
[3.18] What is a Hamiltonian Chaos?
[4]   To Learn More
[4.1] What should I read to learn more?
[4.2] What technical journals have nonlinear science articles?
[4.3] What are net sites for nonlinear science materials?
[5]   Computational Resources
[5.1] What are general computational resources?
[5.2] Where can I find specialized programs for  nonlinear science?
[6] Acknowledgments

[2]   Basic Theory
[2.1] What is nonlinear?

In geometry, linearity refers to Euclidean objects: lines, planes, (flat) 
three-dimensional space, etc.--these objects appear the same no matter how we 
examine them. A nonlinear object, a sphere for example, looks different on 
different scales--when looked at closely enough it looks like a plane, and 
from a far enough distance it looks like a point. 

In algebra, we define linearity in terms of functions that have the property 
f(x+y) = f(x)+f(y) and f(ax) = af(x). Nonlinear is defined as the negation of 
linear. This means that the result f may be out of proportion to the input x 
or y. The result may be more than linear, as when a diode begins to pass 
current; or less than linear, as when finite resources limit Malthusian 
population growth. Thus the fundamental simplifying tools of linear analysis 
are no longer available: for example, for a linear system, if we have two 
zeros, f(x) = 0 and f(y) = 0, then we automatically have a third zero f(x+y) = 
0 (in fact there are infinitely many zeros as well, since linearity implies 
that f(ax+by) = 0 for any a and b). This is called the principle of 
superposition--it gives many solutions from a few. For nonlinear systems, each 
solution must be fought for (generally) with unvarying ardor! 

[2.2] What is nonlinear science?

Stanislaw Ulam reportedly said (something like) "Calling a science 'nonlinear' 
is like calling zoology 'the study of non-human animals'. So why do we have a 
name that appears to be merely a negative? 

Firstly, linearity is rather special, and no model of a real system is truly 
linear. Some things are profitably studied as linear approximations to the 
real models--for example the fact that Hooke's law, the linear law of 
elasticity (strain is proportional to stress) is approximately valid for a 
pendulum of small amplitude implies that its period is approximately 
independent of amplitude. However, as the amplitude gets large the period gets 
longer, a fundamental effect of nonlinearity in the pendulum equations (see and [3.10]).

(You might protest that quantum mechanics is the fundamental theory and that 
it is linear! However this is at the expense of infinite dimensionality which 
is just as bad or worse--and 'any' finite dimensional nonlinear model can be 
turned into an infinite dimensional linear one--e.g. a map x' = f(x) is 
equivalent to the linear integral equation often called the Perron-Frobenius 
p'(x) = integral [ p(y) \delta(x-f(y)) dy ]) 
Here p(x) is a density, which could be interpreted as the probability of 
finding oneself at the point x, and the Dirac-delta function effectively moves 
the points according to the map f to give the new density. So even a nonlinear 
map is equivalent to a linear operator.)

Secondly, nonlinear systems have been shown to exhibit surprising and complex 
effects that would never be anticipated by a scientist trained only in linear 
techniques. Prominent examples of these include bifurcation, chaos, and 
solitons. Nonlinearity has its most profound effects on dynamical systems (see 

Further, while we can enumerate the linear objects, nonlinear ones are 
nondenumerable, and as of yet mostly unclassified. We currently have no 
general techniques (and very few special ones) for telling whether a 
particular nonlinear system will exhibit the complexity of chaos, or the 
simplicity of order. Thus since we cannot yet subdivide nonlinear science into 
proper subfields, it exists as a whole. 

Nonlinear science has applications to a wide variety of fields, from 
mathematics, physics, biology, and chemistry, to engineering, economics, and 
medicine. This is one of its most exciting aspects--that it brings researchers 
from many disciplines together with a common language.

[2.3] What is a dynamical system?

A dynamical system consists of an abstract phase space or state space, whose 
coordinates describe the dynamical state at any instant; and a dynamical rule 
which specifies the immediate future trend of all state variables, given only 
the present values of those same state variables. Mathematically, a dynamical 
system is described by an initial value problem. 

Dynamical systems are "deterministic" if there is a unique consequent to every 
state, and "stochastic" or "random" if there is more than one consequent 
chosen from some probability distribution (the "perfect" coin toss has two 
consequents with equal probability for each initial state). Most of nonlinear 
science--and everything in this FAQ--deals with deterministic systems.

A dynamical system can have discrete or continuous time. The discrete case is 
defined by a map, z_1 = f(z_0), that gives the state z_1 resulting from the 
initial state z_0 at the next time value. The continuous case is defined by a 
"flow", z(t) = \phi_t(z_0), which gives the state at time t, given that the 
state was z_0 at time 0. A smooth flow can be differentiated w.r.t. time to 
give a differential equation, dz/dt = F(z). In this case we call F(z) a 
"vector field," it gives a vector pointing in the direction of the velocity at 
every point in phase space.

[2.4] What is phase space?

Phase space is the collection of possible states of a dynamical system. A 
phase space can be finite (e.g. for the ideal coin toss, we have two states 
heads and tails), countably infinite (e.g. state variables are integers), or 
uncountably infinite (e.g. state variables are real numbers). Implicit in the 
notion is that a particular state in phase space specifies the system 
completely; it is all we need to know about the system to have complete 
knowledge of the immediate future. Thus the phase space of the planar pendulum 
is two-dimensional, consisting of the position (angle) and velocity. According 
to Newton, specification of these two variables uniquely determines the 
subsequent motion of the pendulum.

Note that if we have a non-autonomous system, where the map or vector field 
depends explicitly on time (e.g. a model for plant growth depending on solar 
flux), then according to our definition of phase space, we must include time 
as a phase space coordinate--since one must specify a specific time (e.g. 3PM 
on Tuesday) to know the subsequent motion. Thus dz/dt = F(z,t) is a dynamical 
system on the phase space consisting of (z,t), with the addition of the new 
dynamics dt/dt = 1.

The path in phase space traced out by a solution of an initial value problem 
is called an orbit or trajectory of the dynamical system. If the state 
variables take real values in a continuum, the orbit of a continuous-time 
system is a curve, while the orbit of a discrete-time system is a sequence of 

[2.5] What is a degree of freedom?

The notion of "degrees of freedom" as it is used for systems means 
one canonical conjugate pair, a configuration, q, and its conjugate momentum 
p. Hamiltonian systems (sometimes mistakenly identified with the notion of 
conservative systems) always have such pairs of variables, and so the phase 
space is even dimensional.

In the study of dissipative systems the term "degree of freedom" is often used 
differently, to mean a single coordinate dimension of the phase space. This 
can lead to confusion, and it is advisable to check which meaning of the term 
is intended in a particular context.

Those with a physics background generally prefer to stick with the Hamiltonian 
definition of the term "degree of freedom." For a more general system the 
proper term is "order" which is equal to the dimension of the phase space.

Note that a  dynamical system with N d.o.f. Hamiltonian nominally moves in a 
2N dimensional phase space. However, if H(q,p) is time independent, then 
energy is conserved, and therefore the motion is really on a 2N-1 dimensional 
energy surface, H(q,p) = E. Thus e.g. the planar, circular restricted 3 body 
problem is 2 d.o.f., and motion is on the 3D energy surface of constant 
"Jacobi constant." It can be reduced to a 2D area preserving map by Poincaré 
section (see [2.6]).

If the Hamiltonian is time dependent, then we generally say it has an 
additional 1/2 degree of freedom, since this adds one dimension to the phase 
space. (i.e. 1 1/2 d.o.f. means three variables, q, p and t, and energy is no 
longer conserved).

[2.6] What is a map?

A map is simply a function, f, on the phase space that gives the next state, 
f(z) (the image), of the system given its current state, z. (Often you will 
find the notation z' = f(z), where the prime means the next point, not the 

Now a function must have a single value for each state, but there could be 
several different states that give rise to the same image. Maps that allow 
every state in the phase space to be accessed (onto) and which have precisely 
one pre-image for each state (one-to-one) are invertible. If in addition the 
map and its inverse are continuous (with respect to the phase space coordinate 
z), then it is called a homeomorphism. A homeomorphism that has at least one 
continuous derivative (w.r.t. z) and a continuously differentiable inverse is 
a diffeomorphism.

Iteration of a map means repeatedly applying the map to the consequents of the 
previous application. Thus we get a sequence
                        z  = f(z   )  = f(f(z   )...) = f (z )
                         n      n-1          n-2            0

This sequence is the orbit or trajectory of the dynamical system with initial 
condition z_0.

[2.7] How are maps related to flows (differential equations)?

Every differential equation gives rise to a map, the time one map, defined by 
advancing the flow one unit of time. This map may or may not be useful. If the 
differential equation contains a term or terms periodic in time, then the time 
T map (where T is the period) is very useful--it is an example of a Poincaré 
section. The time T map in a system with periodic terms is also called a 
stroboscopic map, since we are effectively looking at the location in phase 
space with a stroboscope tuned to the period T. This map is useful because it 
permits us to dispense with time as a phase space coordinate: the remaining 
coordinates describe the state completely so long as we agree to consider the 
same instant within every period.

In autonomous systems (no time-dependent terms in the equations), it may also 
be possible to define a Poincaré section and again reduce the phase space 
dimension by one. Here the Poincaré section is defined not by a fixed time 
interval, but by successive times when an orbit crosses a fixed surface in 
phase space. (Surface here means a manifold of dimension one less than the 
phase space dimension).

However, not every flow has a global Poincaré section (e.g. any flow with an 
equilibrium point), which would need to be transverse to every possible orbit.

Maps arising from stroboscopic sampling or Poincaré section of a flow are 
necessarily invertible, because the flow has a unique solution through any 
point in phase space--the solution is unique both forward and backward in 
time. However, noninvertible maps can be relevant to differential equations: 
Poincaré maps are sometimes very well approximated by noninvertible maps. For 
example, the Henon map (x,y) -> (-y-a+x^2,bx) with small |b| is close to the 
logistic map, x -> -a+x^2.

It is often (though not always) possible to go backwards, from an invertible 
map to a differential equation having the map as its Poincaré map. This is 
called a suspension of the map. One can also do this procedure approximately 
for maps that are close to the identity, giving a flow that approximates the 
map to some order. This is extremely useful in bifurcation theory.

Note that any numerical solution procedure for a differential initial value 
problem which uses discrete time steps in the approximation is effectively a 
map. This is not a trivial observation; it helps explain for example why a 
continuous-time system which should not exhibit chaos may have numerical 
solutions which do--see [2.15].

[2.8] What is an attractor?

Informally an attractor is simply a state into which a system settles (thus 
dissipation is needed). Thus in the long term, a dissipative dynamical system 
may settle into an attractor.
   Interestingly enough, there is still some controversy in the mathematics 
community as to an appropriate definition of this term. Most people adopt the 
Attractor: A set in the phase space that has a neighborhood in which every 
point stays nearby and approaches the attractor as time goes to infinity.
Thus imagine a ball rolling inside of a bowl. If we start the ball at a point 
in the bowl with a velocity too small to reach the edge of the bowl, then 
eventually the ball will settle down to the bottom of the bowl with zero 
velocity: thus this equilibrium point is an attractor. The neighborhood of 
points that eventually approach the attractor is the basin of attraction for 
the attractor. In our example the basin is the set of all configurations 
corresponding to the ball in the bowl, and for each such point all small 
enough velocities (it is a set in the four dimensional phase space [2.4]).
   Attractors can be simple, as the previous example. Another example of an 
attractor is a limit cycle, which is a periodic orbit that is attracting 
(limit cycles can also be repelling). More surprisingly, attractors can be 
chaotic (see [2.9]) and/or strange (see [2.12]).
   The boundary of a basin of attraction is often a very interesting object 
since it distinguishes between different types of motion. Typically a basin 
boundary is a saddle orbit, or such an orbit and its stable manifold. A crisis 
is the change in an attractor when its basin boundary is destroyed.
   An alternative definition of attractor is sometimes used because there 
are systems that have sets that attract most, but not all, initial conditions 
in their neighborhood (such phenomena is sometimes called riddling of the 
basin). Thus, Milnor defines an attractor as a set for which a positive 
measure (probability, if you like) of initial conditions in a neighborhood are 
asymptotic to the set.

[2.9] What is chaos?

It has been said that "Chaos is a name for any order that produces confusion 
in our minds." (George Santayana, thanks to Fred Klingener for finding this). 
However, the mathematical definition is, roughly speaking, 
Chaos: effectively unpredictable long time behavior arising in a deterministic 
dynamical system because of sensitivity to initial conditions.
It must be emphasized that a deterministic dynamical system is perfectly 
predictable given perfect knowledge of the initial condition, and is in 
practice always predictable in the short term. The key to long-term 
unpredictability is a property known as sensitivity to (or sensitive 
dependence on) initial conditions.

For a dynamical system to be chaotic it must have a 'large' set of initial 
conditions which are highly unstable. No matter how precisely you measure the 
initial condition in these systems, your prediction of its subsequent motion 
goes radically wrong after a short time. Typically (see [2.14] for one 
definition of 'typical'), the predictability horizon grows only 
logarithmically with the precision of measurement (for positive Lyapunov 
exponents, see [2.11]). Thus for each increase in precision by a factor of 10, 
say, you may only be able to predict two more time units (measured in units of 
the Lyapunov time, i.e. the inverse of the Lyapunov exponent).

More precisely: A map f is chaotic on a compact invariant set S if
   (i) f is transitive on S (there is a point x whose orbit is dense in S), and
   (ii) f exhibits sensitive dependence on S (see [2.10]).
To these two requirements #DevaneyDevaney adds the requirement that periodic 
points are dense in S, but this doesn't seem to be really in the spirit of the 
notion, and is probably better treated as a theorem (very difficult and very 
important), and not part of the definition.

Usually we would like the set S to be a large set. It is too much to hope for 
except in special examples that S be the entire phase space. If the dynamical 
system is dissipative then we hope that S is an attractor (see [2.8]) with a 
large basin. However, this need not be the case--we can have a chaotic saddle, 
an orbit that has some unstable directions as well as stable directions.

As a consequence of long-term unpredictability, time series from chaotic 
systems may appear irregular and disorderly. However, chaos is definitely not 
(as the name might suggest) complete disorder; it is disorder in a 
deterministic dynamical system, which is always predictable for short times.

The notion of chaos seems to conflict with that attributed to Laplace: given 
precise knowledge of the initial conditions, it should be possible to predict 
the future of the universe. However, Laplace's dictum is certainly true for 
any deterministic system, recall [2.3]. The main consequence of chaotic motion 
is that given imperfect knowledge, the predictability horizon in a 
deterministic system is much shorter than one might expect, due to the 
exponential growth of errors. The belief that small errors should have small 
consequences was perhaps engendered by the success of Newton's mechanics 
applied to planetary motions. Though these happen to be regular on human 
historic time scales, they are chaotic on the 5 million year time scale (see 
e.g. "Newton's Clock", by Ivars Peterson (1993 W.H. Freeman).

[2.10] What is sensitive dependence on initial conditions?

Consider a boulder precariously perched on the top of an ideal hill. The 
slightest push will cause the boulder to roll down one side of the hill or the 
other: the subsequent behavior depends sensitively on the direction of the 
push--and the push can be arbitrarily small.  Of course, it is of great 
importance to you which direction the boulder will go if you are standing at 
the bottom of the hill on one side or the other!

Sensitive dependence is the equivalent behavior for every initial condition--
every point in the phase space is effectively perched on the top of a hill.

More precisely a set S exhibits sensitive dependence if there is an r such 
that for any epsilon > 0 and for each x in S, there is a y such that |x - y| < 
epsilon, and |x_n - y_n| > r for some n > 0. Then there is a fixed distance r 
(say 1), such that no matter how precisely one specifies an initial state 
there are nearby states that eventually get a distance r away.

Note: sensitive dependence does not require exponential growth of 
perturbations (positive Lyapunov exponent), but this is typical (see [2.14]) 
for chaotic systems. Note also that we most definitely do not require ALL 
nearby initial points diverge--generically [2.14] this does not happen--some 
nearby points may converge. (We may modify our hilltop analogy slightly and 
say that every point in phase space acts like a high mountain pass.) Finally, 
the words "initial conditions" are a bit misleading: a typical small 
disturbance introduced at any time will grow similarly. Think of "initial" as 
meaning "a time when a disturbance or error is introduced," not necessarily 
time zero.

[2.11] What are Lyapunov exponents?
(Thanks to Ronnie Mainieri & Fred Klingener for contributing to this answer)

The hardest thing to get right about Lyapunov exponents is the spelling of 
Lyapunov, which you will variously find as Liapunov, Lyapunof and even 
Liapunoff. Of course Lyapunov is really spelled in the Cyrillic alphabet: 
(Lambda)(backwards r)(pi)(Y)(H)(0)(B). Now that there is an ANSI standard of 
transliteration for Cyrillic, we expect all references to converge on the 
version Lyapunov.

Lyapunov was born in Russia in 6 June 1857. He was greatly influenced by 
Chebyshev and was a student with Markov. He was also a passionate man: 
Lyapunov shot himself the day his wife died. He died 3 Nov. 1918, three days 
later. According to the request on a note he left, Lyapunov was buried with 
his wife. [biographical data from a biography by A. T. Grigorian].

Lyapunov left us with more than just a simple note. He left a collection of 
papers on the equilibrium shape of rotating liquids, on probability, and on 
the stability of low-dimensional dynamical systems. It was from his 
dissertation that the notion of Lyapunov exponent emerged. Lyapunov was 
interested in showing how to discover if a solution to a dynamical system is 
stable or not for all times. The usual method of studying stability, i.e. 
linear stability, was not good enough, because if you waited long enough the 
small errors due to linearization would pile up and make the approximation 
invalid. Lyapunov developed concepts (now called Lyapunov Stability) to 
overcome these difficulties.

Lyapunov exponents measure the rate at which nearby orbits converge or 
diverge. There are as many Lyapunov exponents as there are dimensions in the 
state space of the system, but the largest is usually the most important. 
Roughly speaking the (maximal) Lyapunov exponent is the time constant, lambda, 
in the expression for the distance between two nearby orbits, exp(lambda * 
t).  If lambda is negative, then the orbits converge in time, and the 
dynamical system is insensitive to initial conditions.  However, if lambda is 
positive, then the distance between nearby orbits grows exponentially in time, 
and the system exhibits sensitive dependence on initial conditions.

There are basically two ways to compute Lyapunov exponents. In one way one 
chooses two nearby points, evolves them in time, measuring the growth rate of 
the distance between them. This is useful when one has a time series, but has 
the disadvantage that the growth rate is really not a local effect as the 
points separate. A better way is to measure the growth rate of tangent vectors 
to a given orbit.

More precisely, consider a map f in an m dimensional phase space, and its 
derivative matrix Df(x). Let v be a tangent vector at the point x. Then we 
define a function
                              1          n
        L(x,v)  =    lim     --- ln |( Df (x)v )|
                   n -> oo    n
Now the Multiplicative Ergodic Theorem of Oseledec states that this limit 
exists for almost all points x and all tangent vectors v. There are at most m 
distinct values of L as we let v range over the tangent space. These are the 
Lyapunov exponents at x.

For more information on computing the exponents see

   Wolf, A., J. B. Swift, et al. (1985). "Determining Lyapunov Exponents from a 
Time Series." Physica D 16: 285-317.
   Eckmann, J.-P., S. O. Kamphorst, et al. (1986). "Liapunov exponents from 
time series." Phys. Rev. A 34: 4971-4979.

[2.12] What is a Strange Attractor?
   Before Chaos (BC?), the only known attractors (see [2.8]) were fixed 
points, periodic orbits (limit cycles), and invariant tori (quasiperiodic 
orbits). In fact the famous Poincaré-Bendixson theorem states that for a pair 
of first order differential equations,  only fixed points and limit cycles can 
occur (there is no chaos in 2D flows). 
   In a famous paper in 1963, Ed Lorenz discovered that simple systems of 
three differential equations can have complicated attractors. The Lorenz 
attractor (with its butterfly wings reminding us of sensitive dependence (see 
[2.10])) is the "icon" of chaos Lorenz showed 
that his attractor was chaotic, since it exhibited sensitive dependence. 
Moreover, his attractor is also "strange," which means that it is a fractal 
(see [3.2]).
   The term strange attractor was introduced by Ruelle and Takens in 1970 
in their discussion of a scenario for the onset of turbulence in fluid flow. 
They noted that when periodic motion goes unstable (with three or more modes), 
the typical (see [2.14]) result will be a geometrically strange object.
   Unfortunately, the term strange attractor is often used for any chaotic 
attractor. However, the term should be reserved for attractors that are 
"geometrically" strange, e.g. fractal. One can have chaotic attractors that 
are not strange (a trivial example would be to take a system like the cat map, 
which has the whole plane as a chaotic set, and add a third dimension which is 
simply contracting onto the plane). There are also strange, nonchaotic 
attractors (see Grebogi, C., et al. (1984). "Strange Attractors that are not 
Chaotic." Physica D 13: 261-268).

[2.13] Can computers simulate chaos?

Strictly speaking, chaos cannot occur on computers because they deal with 
finite sets of numbers. Thus the initial condition is always precisely known, 
and computer experiments are perfectly predictable, in principle. In 
particular because of the finite size, every trajectory computed will 
eventually have to repeat (an thus be eventually periodic). On the other hand, 
computers can effectively simulate chaotic behavior for quite long times (just 
so long as the discreteness is not noticeable). In particular if one uses 
floating point numbers in double precision to iterate a map on the unit 
square, then there are about 10^28 different points in the phase space, and 
one would expect the "typical" chaotic orbit to have a period of about 10^14 
(this square root of the number of points estimate is given by Rannou for 
random diffeomorphisms and does not really apply to floating point operations, 
but nonetheless the period should be a big number). See, e.g.,

   Earn, D. J. D. and S. Tremaine, "Exact Numerical Studies of Hamiltonian 
Maps: Iterating without Roundoff Error," Physica D 56, 1-22 (1992). 
   Binder, P. M. and R. V. Jensen, "Simulating Chaotic Behavior with Finite 
State Machines," Phys. Rev. 34A, 4460-3 (1986).
   Rannou, F., "Numerical Study of Discrete Plane Area-Preserving Mappings," 
Astron. and Astrophys. 31, 289-301 (1974).

[2.14] What is generic?
(Thanks to Hawley Rising for contributing to this answer)

Generic in dynamical systems is intended to convey "usual" or, more properly, 
"observable". Roughly speaking, a property is generic over a class if any 
system in the class can be modified ever so slightly (perturbed), into one 
with that property.

The formal definition is done in the language of topology: Consider the class 
to be a space of systems, and suppose it has a topology (some notion of a 
neighborhood, or an open set). A subset of this space is dense if its closure 
(the subset plus the limits of all sequences in the subset) is the whole 
space. It is open and dense if it is also an open set (union of 
neighborhoods). A set is countable if it can be put into 1-1 correspondence 
with the counting numbers. A countable intersection of open dense sets is the 
intersection of a countable number of open dense sets. If all such 
intersections in a space are also dense, then the space is called a Baire 
space, which basically means it is big enough. If we have such a Baire space 
of dynamical systems, and there is a property which is true on a countable 
intersection of open dense sets, then that property is generic.

If all this sounds too complicated, think of it as a precise way of defining a 
set which is near every system in the collection (dense), which isn't too big 
(need not have any "regions" where the property is true for every system). 
Generic is much weaker than "almost everywhere" (occurs with probability 1), 
in fact, it is possible to have generic properties which occur with 
probability zero. But it is as strong a property as one can define 
topologically, without having to have a property hold true in a region, or 
talking about measure (probability), which isn't a topological property (a 
property preserved by a continuous function).

[2.15] What is the minimum phase space dimension for chaos?

This is a slightly confusing topic, since the answer depends on the type of 
system considered. First consider a flow (or system of differential 
equations). In this case the Poincaré-Bendixson theorem tells us that there is 
no chaos in one or two-dimensional phase spaces. Chaos is possible in three-
dimensional flows--standard examples such as the Lorenz equations are indeed 
three-dimensional, and there are mathematical 3D flows that are provably 
chaotic (e.g. the 'solenoid').

Note: if the flow is non-autonomous then time is a phase space coordinate, so 
a system with two physical variables + time becomes three-dimensional, and 
chaos is possible (i.e. Forced second-order oscillators do exhibit chaos.)

For maps, it is possible to have chaos in one dimension, but only if the map 
is not invertible. A prominent example is the Logistic map
                    x' = f(x) = rx(1-x).
This is provably chaotic for r = 4, and many other values of r as well (see 
e.g. #DevaneyDevaney). Note that every point x < f(1/2) has two preimages, so 
this map is not invertible.

For homeomorphisms, we must have at least two-dimensional phase space for 
chaos. This is equivalent to the flow result, since a three-dimensional flow 
gives rise to a two-dimensional homeomorphism by Poincaré section (see [2.7]).

Note that a numerical algorithm for a differential equation is a map, because 
time on the computer is necessarily discrete. Thus numerical solutions of two 
and even one dimensional systems of ordinary differential equations may 
exhibit chaos. Usually this results from choosing the size of the time step 
too large. For example Euler discretization of the Logistic differential 
equation, dx/dt = rx(1-x), is equivalent to the logistic map. See e.g. S. 
Ushiki, "Central difference scheme and chaos," Physica 4D (1982) 407-424.

[3]   Applications and Advanced Theory
[3.1] What are complex systems?
(Thanks to Troy Shinbrot for contributing to this answer)

Complex systems are spatially and/or temporally extended nonlinear systems 
characterized by collective properties associated with the system as a whole--
and that are different from the characteristic behaviors of the constituent 

While, chaos is the study of how simple systems can generate complicated 
behavior, complexity is the study of how complicated systems can generate 
simple behavior. An example of complexity is the synchronization of biological 
systems ranging from fireflies to neurons (e.g. Matthews, PC, Mirollo, RE & 
Strogatz, SH "Dynamics of a large system of coupled nonlinear oscillators," 
Physica 52D (1991) 293-331). In these problems, many individual systems 
conspire to produce a single collective rhythm.

The notion of complex systems has received lots of popular press, but it is 
not really clear as of yet if there is a "theory" about a "concept". We are 
withholding judgment. See The Complexity & Artificial Life Web Site The self-organized systems FAQ

[3.2] What are fractals?

One way to define "fractal" is as a negation: a fractal is a set that does not 
look like a Euclidean object (point, line, plane, etc.) no matter how closely 
you look at it. Imagine focusing in on a smooth curve (imagine a piece of 
string in space)--if you look at any piece of it closely enough it eventually 
looks like a straight line (ignoring the fact that for a real piece of string 
it will soon look like a cylinder and eventually you will see the fibers, then 
the atoms, etc.). A fractal, like the Koch Snowflake, which is topologically 
one dimensional, never looks like a straight line, no matter how closely you 
look. There are indentations, like bays in a coastline; look closer and the 
bays have inlets, closer still the inlets have subinlets, and so on. Simple 
examples of fractals include Cantor sets (see [3.5], Sierpinski curves, the 
Mandelbrot set  and (almost surely) the Lorenz attractor (see [2.12]). 
Fractals also approximately describe many real-world objects, such as clouds 
(see  mountains, turbulence, 
coastlines, roots and branches of trees and veins and lungs of animals.

"Fractal" is a term which has undergone refinement of definition by a lot of 
people, but was first coined by B. Mandelbrot,,  and defined 
as a set with fractional (non-integer) dimension (Hausdorff dimension, see 
[3.4]). Mandelbrot defines a fractal in the following way:

    A geometric figure or natural object is said to be fractal if it
    combines the following characteristics: (a) its parts have the same
    form or structure as the whole, except that they are at a different
    scale and may be slightly deformed; (b) its form is extremely irregular,
    or extremely interrupted or fragmented, and remains so, whatever the scale
    of examination; (c) it contains "distinct elements" whose scales are very
    varied and cover a large range." (Les Objets Fractales 1989, p.154) 

See the extensive FAQ from sci.fractals at

[3.3] What do fractals have to do with chaos?

Often chaotic dynamical systems exhibit fractal structures in phase space. 
However, there is no direct relation. There are chaotic systems that have 
nonfractal limit sets (e.g. Arnold's cat map) and fractal structures that can 
arise in nonchaotic dynamics (see e.g. Grebogi, C., et al. (1984). "Strange 
Attractors that are not Chaotic." Physica 13D: 261-268.)

[3.4] What are topological and fractal dimension?

See the fractal FAQ:
or the site

[3.5] What is a Cantor set?
(Thanks to Pavel Pokorny for contributing to this answer)

A Cantor set is a surprising set of points that is both infinite (uncountably 
so, see [2.14]) and yet diffuse. It is a simple example of a fractal, and 
occurs, for example as the strange repellor in the logistic map (see [2.15]) 
when r>4. The standard example of a Cantor set is the "middle thirds" set 
constructed on the interval between 0 and 1. First, remove the middle third. 
Two intervals remain, each one of length one third. From each remaining 
interval remove the middle third. Repeat the last step infinitely many times. 
What remains is a Cantor set.

More generally (and abstrusely) a Cantor set is defined topologically as a 
nonempty, compact set which is perfect (every point is a limit point) and 
totally disconnected (every pair of points in the set are contained in 
disjoint covering neighborhoods).

See also

Georg Ferdinand Ludwig Philipp Cantor was born 3 March 1845 in St Petersburg, 
Russia, and died 6 Jan 1918 in Halle, Germany. To learn more about him see:

To read more about the Cantor function (a function that is continuous, 
differentiable, increasing, non-constant, with a derivative that is zero 
everywhere except on a set with length zero) see

[3.6] What is quantum chaos?
(Thanks to Leon Poon for contributing to this answer)

 According to the correspondence principle, there is a limit where classical 
behavior as described by Hamilton's equations becomes similar, in some 
suitable sense, to quantum behavior as described by the appropriate wave 
equation. Formally, one can take this limit to be h -> 0, where h is Planck's 
constant; alternatively, one can look at successively higher energy levels. 
Such limits are referred to as "semiclassical". It has been found that the 
semiclassical limit can be highly nontrivial when the classical problem is 
chaotic. The study of how quantum systems, whose classical counterparts are 
chaotic, behave in the semiclassical limit has been called quantum chaos. More 
generally, these considerations also apply to elliptic partial differential 
equations that are physically unrelated to quantum considerations. For 
example, the same questions arise in relating classical waves to their 
corresponding ray equations. Among recent results in quantum chaos is a 
prediction relating the chaos in the classical problem to the statistics of 
energy-level spacings in the semiclassical quantum regime.

Classical chaos can be used to analyze such ostensibly quantum systems as the 
hydrogen atom, where classical predictions of microwave ionization thresholds 
agree with experiments. See Koch, P. M. and K. A. H. van Leeuwen (1995). 
"Importance of Resonances in Microwave Ionization of Excited Hydrogen Atoms." 
Physics Reports 255: 289-403.

See also: Quantum Chaos  Microlaser 

[3.7] How do I know if my data are deterministic?
(Thanks to Justin Lipton for contributing to this answer)

How can I tell if my data is deterministic? This is a very tricky problem. It 
is difficult because in practice no time series consists of pure 'signal.' 
There will always be some form of corrupting noise, even if it is present as 
round-off or truncation error or as a result of finite arithmetic or 
quantization. Thus any real time series, even if mostly deterministic, will be 
a stochastic processes

All methods for distinguishing deterministic and stochastic processes rely on 
the fact that a deterministic system will always evolve in the same way from a 
given starting point. Thus given a time series that we are testing for 
determinism we
   (1) pick a test state
   (2) search the time series for a similar or 'nearby' state and
   (3) compare their respective time evolution.

Define the error as the difference between the time evolution of the 'test' 
state and the time evolution of the nearby state. A deterministic system will 
have an error that either remains small (stable, regular solution) or increase 
exponentially with time (chaotic solution). A stochastic system will have a 
randomly distributed error.

Essentially all measures of determinism taken from time series rely upon 
finding the closest states to a given 'test' state (i.e., correlation 
dimension, Lyapunov exponents, etc.). To define the state of a system one 
typically relies on phase space embedding methods, see [3.14].

Typically one chooses an embedding dimension, and investigates the propagation 
of the error between two nearby states. If the error looks random, one 
increases the dimension. If you can increase the dimension to obtain a 
deterministic looking error, then you are done. Though it may sound simple it 
is not really! One complication is that as the dimension increases the search 
for a nearby state requires a lot more computation time and a lot of data (the 
amount of data required increases exponentially with embedding dimension) to 
find a suitably close candidate. If the embedding dimension (number of 
measures per state) is chosen too small (less than the 'true' value) 
deterministic data can appear to be random but in theory there is no problem 
choosing the dimension too large--the method will work. Practically, anything 
approaching about 10 dimensions is considered so large that a stochastic 
description is probably more suitable and convenient anyway.

See e.g.,
   Sugihara, G. and R. M. May (1990). "Nonlinear Forecasting as a Way of 
      Distinguishing Chaos from Measurement Error in Time Series." Nature 
344: 734-740.

[3.8] What is the control of chaos?

Control of chaos has come to mean the two things:
   stabilization of unstable periodic orbits,
   use of recurrence to allow stabilization to be applied locally.
Thus term "control of chaos" is somewhat of a misnomer--but the name has 
stuck. The ideas for controlling chaos originated in the work of Hubler 
followed by the Maryland Group.

   Hubler, A. W. (1989). "Adaptive Control of Chaotic Systems." Helv. Phys. 
Acta 62: 343-346.
   Ott, E., C. Grebogi, et al. (1990). "Controlling Chaos." Physical Review 
Letters 64(11): 1196-1199. http://www-

The idea that chaotic systems can in fact be controlled may be 
counterintuitive--after all they are unpredictable in the long term. 
Nevertheless, numerous theorists have independently developed methods which 
can be applied to chaotic systems, and many experimentalists have demonstrated 
that physical chaotic systems respond well to both simple and sophisticated 
control strategies. Applications have been proposed in such diverse areas of 
research as communications, electronics, physiology, epidemiology, fluid 
mechanics and chemistry.

The great bulk of this work has been restricted to low-dimensional systems; 
more recently, a few researchers have proposed control techniques for 
application to high- or infinite-dimensional systems. The literature on the 
subject of the control of chaos is quite voluminous; nevertheless several 
reviews of the literature are available, including:

   Shinbrot, T. Ott, E., Grebogi, C. & Yorke, J.A., "Using Small Perturbations 
to Control Chaos," Nature, 363 (1993) 411-7.
   Shinbrot, T., "Chaos: Unpredictable yet Controllable?" Nonlin. Sciences 
Today, 3:2 (1993) 1-8.
   Shinbrot, T., "Progress in the Control of Chaos," Advance in Physics (in 
   Ditto, WL & Pecora, LM "Mastering Chaos," Scientific American (Aug. 1993), 
   Chen, G. & Dong, X, "From Chaos to Order -- Perspectives and Methodologies 
in Controlling Chaotic Nonlinear Dynamical Systems," Int. J. Bif. & Chaos 3 
(1993) 1363-1409.

It is generically quite difficult to control high dimensional systems; an 
alternative approach is to use control to reduce the dimension before applying 
one of the above techniques. This approach is in its infancy; see:

   Auerbach, D., Ott, E., Grebogi, C., and Yorke, J.A. "Controlling Chaos in
   High Dimensional Systems," Phys. Rev. Lett. 69  (1992) 3479-82

[3.9] How can I build a chaotic circuit?
(Thanks to Justin Lipton and Jose Korneluk for contributing to this answer)

There are many different physical systems which display chaos, dripping 
faucets, water wheels, oscillating magnetic ribbons etc. but the most simple 
systems which can be easily implemented are chaotic circuits. In fact an 
electronic circuit was one of the first demonstrations of chaos which showed 
that chaos is not just a mathematical abstraction. Leon Chua designed the 
circuit 1983.

The circuit he designed, now known as Chua's circuit, consists of a piecewise 
linear resistor as its nonlinearity (making analysis very easy) plus two 
capacitors, one resistor and one inductor--the circuit is unforced 
(autonomous). In fact the chaotic aspects (bifurcation values, Lyapunov 
exponents, various dimensions etc.) of this circuit have been extensively 
studied in the literature both experimentally and theoretically. It is 
extremely easy to build and presents beautiful attractors (see [2.8]) (the 
most famous known as the double scroll attractor) that can be displayed on a 

For more information on building such a circuit try: see  Chua's Circuit  Applet

   Matsumoto T. and Chua L.O. and Komuro M. "Birth and Death of the Double 
      Scroll" Physica D24 97-124, 1987.
   Kennedy M. P., "Robust OP Amp Realization of Chua's Circuit", Frequenz 
      46, no. 3-4, 1992
   Madan, R. A., Chua's Circuit: A paradigm for chaos, ed. R. A. Madan, 
      Singapore: World Scientific, 1993.
   Pecora, L. and Carroll, T. Nonlinear Dynamics in Circuits, Singapore: 
      World Scientific, 1995.
   Nonlinear Dynamics of Electronic Systems, Proceedings of the Workshop 
      NDES 1993, A.C.Davies and W.Schwartz, eds., World Scientific, 1994, 
      ISBN 981-02-1769-2.
   Parker, T.S., and L.O.Chua, Practical Numerical Algorithms for Chaotic 
      Systems, Springer-Verlag, 1989, ISBN's: 0-387-96689-7 
      and 3-540-96689-7.

[3.10] What are simple experiments to demonstrate chaos?

 There are many "chaos toys" on the market. Most consist of some sort of 
pendulum that is forced by an electromagnet. One can of course build a simple 
double pendulum to observe beautiful chaotic behavior see 
Experimental Pendulum Designs  Java 
Applet Java Applets Pendulum Lab

My favorite double pendulum consists of two identical planar pendula, so that 
you can demonstrate sensitive dependence [2.10], for a Java applet simulation 
see Another cute toy is 
the "Space Circle" that you can find in many airport gift shops. This is 
discussed in the article:

   A. Wolf & T. Bessoir, Diagnosing Chaos in the Space Circle, Physica 50D, 

One of the simplest chemical systems that shows chaos is the Belousov-
Zhabotinsky reaction. The book by Strogatz [4.1] has a good introduction to 
this subject,. For the recipe see Chemical chaos is modeled 
(in a generic sense) by the "Brusselator" system of differential equations. 

   Nicolis, Gregoire & Prigogine, (1989) Exploring Complexity: An 
      Introduction W. H. Freeman

The Chaotic waterwheel, while not so simple to build, is an exact realization 
of Lorenz famous equations. This is nicely discussed in Strogatz book [4.1] as 

Billiard tables can exhibit chaotic motion, see, though it might be hard to see 
this next time you are in a bar, since a rectangular table is not chaotic!

[3.11] What is targeting?
(Thanks to Serdar Iplikçi for contributing to this answer)

Targeting is the task of steering a chaotic system from any initial point to 
the target, which can be either an unstable equilibrium point or an unstable 
periodic orbit, in the shortest possible time, by applying relatively small 
perturbations. In order to effectively control chaos, [3.8] a targeting 
strategy is important. See:

   Kostelich, E., C. Grebogi, E. Ott, and J. A. Yorke, "Higher
      Dimensional Targeting," Phys Rev. E,. 47, , 305-310 (1993).
   Barreto, E., E. Kostelich, C. Grebogi, E. Ott, and J. A. Yorke, "Efficient
      Switching Between Controlled Unstable Periodic Orbits in Higher
      Dimensional Chaotic Systems," Phys Rev E, 51, 4169-4172 (1995).

One application of targeting is to control a spacecraft's trajectory so that 
one can find low energy orbits from one planet to another. Recently targeting 
techniques have been used in the design of trajectories to asteroids and even 
of a grand tour of the planets. For example,

   E. Bollt and J. D. Meiss, "Targeting Chaotic Orbits to the Moon 
      Through Recurrence," Phys. Lett. A  204, 373-378 (1995).

 [3.12] What is time series analysis?
(Thanks to Jim Crutchfield for contributing to this answer)

This is the application of dynamical systems techniques to a data series, 
usually obtained by "measuring" the value of a single observable as a function 
of time. The major tool in a dynamicist's toolkit is "delay coordinate 
embedding" which creates a phase space portrait from a single data series. It 
seems remarkable at first, but one can reconstruct a picture equivalent 
(topologically) to the full Lorenz attractor (see [2.12])in three-dimensional 
space by measuring only one of its coordinates, say x(t), and plotting the 
delay coordinates (x(t), x(t+h), x(t+2h)) for a fixed h.

It is important to emphasize that the idea of using derivatives or delay 
coordinates in time series modeling is nothing new. It goes back at least to 
the work of Yule, who in 1927 used an autoregressive (AR) model to make a 
predictive model for the sunspot cycle. AR models are nothing more than delay 
coordinates used with a linear model. Delays, derivatives, principal 
components, and a variety of other methods of reconstruction have been widely 
used in time series analysis since the early 50's, and are described in 
several hundred books. The new aspects raised by dynamical systems theory are 
(i) the implied geometric view of temporal behavior and (ii) the existence of 
"geometric invariants", such as dimension and Lyapunov exponents. The central 
question was not whether delay coordinates are useful for time series 
analysis, but rather whether reconstruction methods preserve the geometry and 
the geometric invariants of dynamical systems. (Packard, Crutchfield, Farmer & 

   G.U. Yule, Phil. Trans. R. Soc. London A 226 (1927) p. 267.
   N.H. Packard, J.P. Crutchfield, J.D. Farmer, and R.S. Shaw, "Geometry
      from a time series", Phys. Rev. Lett. 45, no. 9 (1980) 712.
   F. Takens, "Detecting strange attractors in fluid turbulence", in: Dynamical 
      Systems and Turbulence, eds. D. Rand and L.-S. Young 
      (Springer, Berlin, 1981)
   Abarbanel, H.D.I., Brown, R., Sidorowich, J.J., and Tsimring, L.Sh.T. 
      "The analysis of observed chaotic data in physical systems",  
      Rev. Modern Physics 65 (1993) 1331-1392.
   D. Kaplan and L. Glass (1995). Understanding Nonlinear Dynamics, 
   E. Peters (1994) Fractal Market Analysis : Applying Chaos Theory to 
      Investment and Economics, Wiley

[3.13] Is there chaos in the stock market?
(Thanks to Bruce Stewart for Contributions to this answer)

In order to address this question, we must first agree what we mean by chaos, 
see [2.9].

In dynamical systems theory, chaos means irregular fluctuations in a 
deterministic system (see [2.3] and [3.7]). This means the system behaves 
irregularly because of its own internal logic, not because of random forces 
acting from outside. Of course, if you define your dynamical system to be the 
socio-economic behavior of the entire planet, nothing acts randomly from 
outside (except perhaps the occasional meteor), so you have a dynamical 
system. But its dimension (number of state variables--see [2.4]) is vast, and 
there is no hope of exploiting the determinism. This is high-dimensional 
chaos, which might just as well be truly random behavior. In this sense, the 
stock market is chaotic, but who cares?

To be useful, economic chaos would have to involve some kind of collective 
behavior which can be fully described by a small number of variables. In the 
lingo, the system would have to be self-organizing, resulting in low- 
dimensional chaos. If this turns out to be true, then you can exploit the low- 
dimensional chaos to make short-term predictions. The problem is to identify 
the state variables which characterize the collective modes. Furthermore, 
having limited the number of state variables, many events now become external 
to the system, that is, the system is operating in a changing environment, 
which makes the problem of system identification very difficult.

If there were such collective modes of fluctuation, market players would 
probably know about them; economic theory says that if many people recognized 
these patterns, the actions they would take to exploit them would quickly 
nullify the patterns. Market participants would probably not need to know 
chaos theory for this to happen. Therefore if these patterns exist, they must 
be hard to recognize because they do not emerge clearly from the sea of noise 
caused by individual actions; or the patterns last only a very short time 
following some upset to the markets; or both.

A number of people and groups have tried to find these patterns. So far the 
published results are negative. There are also commercial ventures involving 
prominent researchers in the field of chaos; we have no idea how well they are 
succeeding, or indeed whether they are looking for low-dimensional chaos. In 
fact it seems unlikely that markets remain stationary long enough to identify 
a chaotic attractor (see [2.12]). If you know chaos theory and would like to 
devote yourself to the rhythms of market trading, you might find a trading 
firm which will give you a chance to try your ideas. But don't expect them to 
give you a share of any profits you may make for them :-) !

In short, anyone who tells you about the secrets of chaos in the stock market 
doesn't know anything useful, and anyone who knows will not tell. It's an 
interesting question, but you're unlikely to find the answer.

On the other hand, one might ask a more general question: is market behavior 
adequately described by linear models, or are there signs of nonlinearity in 
financial market data? Here the prospect is more favorable. Time series 
analysis (see [3.14]) has been applied these tests to financial data; the 
results often indicate that nonlinear structure is present. See e.g. the book 
by Brock, Hsieh, LeBaron, "Nonlinear Dynamics, Chaos, and Instability", MIT 
Press, 1991; and an update by B. LeBaron, "Chaos and nonlinear forecastability 
in economics and finance," Philosophical Transactions of the Royal Society, 
Series A, vol 348, Sept 1994, pp 397-404. This approach does not provide a 
formula for making money, but it is stimulating some rethinking of economic 
modeling. A book by Richard M. Goodwin, "Chaotic Economic Dynamics," Oxford 
UP, 1990, begins to explore the implications for business cycles.

[3.14] What are solitons?

The process of obtaining a solution of a linear (constant coefficient) 
differential equations is simplified by the Fourier transform (it converts 
such an equation to an algebraic equation, and we all know that algebra is 
easier than calculus!); is there a counterpart which similarly simplifies 
nonlinear equations? The answer is No. Nonlinear equations are qualitatively 
more complex than linear equations, and a procedure which gives the dynamics 
as simply as for linear equations must contain a mistake. There are, however, 
exceptions to any rule.

Certain nonlinear differential equations can be fully solved by, e.g., the 
"inverse scattering method." Examples are the Korteweg-de Vries, nonlinear 
Schrodinger, and sine-Gordon equations. In these cases the real space maps, in 
a rather abstract way, to an inverse space, which is comprised of continuous 
and discrete parts and evolves linearly in time. The continuous part typically 
corresponds to radiation and the discrete parts to stable solitary waves, i.e. 
pulses, which are called solitons. The linear evolution of the inverse space 
means that solitons will emerge virtually unaffected from interactions with 
anything, giving them great stability.

More broadly, there is a wide variety of systems which support stable solitary 
waves through a balance of dispersion and nonlinearity. Though these systems 
may not be integrable as above, in many cases they are close to systems which 
are, and the solitary waves may share many of the stability properties of true 
solitons, especially that of surviving interactions with other solitary waves 
(mostly) unscathed. It is widely accepted to call these solitary waves 
solitons, albeit with qualifications.

Why solitons? Solitons are simply a fundamental nonlinear wave phenomenon. 
Many very basic linear systems with the addition of the simplest possible or 
first order nonlinearity support solitons; this universality means that 
solitons will arise in many important physical situations. Optical fibers can 
support solitons, which because of their great stability are an ideal medium 
for transmitting information. In a few years long distance telephone 
communications will likely be carried via solitons.

The soliton literature is by now vast. Two books which contain clear 
discussions of solitons as well as references to original papers are
   A. C. Newell, Solitons in Mathematics and Physics, SIAM, Philadelphia,
      Penn. (1985)
   M.J. Ablowitz and P.A.Clarkson, Solitons, nonlinear evolution equations and 
      scattering, Cambridge (1991).

[3.15] What is spatio-temporal chaos?

   Spatio-temporal chaos occurs when system of coupled dynamical systems 
gives rise to dynamical behavior that exhibits both spatial disorder (as in 
rapid decay of spatial correlations) and temporal disorder (as in nonzero 
Lyapunov exponents). This is an extremely active, and rather unsettled area of 
research. For an introduction see:
   Cross, M. C. and P. C. Hohenberg (1993). "Pattern Formation outside of
       Equilibrium."  Rev. Mod. Phys. 65: 851-1112. Spatio-Temporal Chaos

An interesting application which exhibits pattern formation and spatio-
temporal chaos is to excitable media in biological or chemical systems. See

   Chaos, Solitions and Fractals 5 #3&4 (1995) Nonlinear Phenomena in Excitable 
      Physiological System, 
      Chaos focus issue on Fibrillation

[3.16] What are cellular automata?
(Thanks to Pavel Pokorny for Contributions to this answer)

   A Cellular automaton (CA) is a dynamical system with discrete time (like 
a map, see [2.6]), discrete state space and discrete geometrical space (like 
an ODE), see [2.7]). Thus they can be represented by a state s(i,j) for 
spatial state i, at time j, where s is taken from some finite set. The update 
rule is that the new state is some function of the old states, s(i,j+1) = 
f(s). The following table shows the distinctions between PDE's, ODE's, coupled 
map lattices (CML) and CA in taking time, state space or geometrical space 
either continuous (C) of discrete (D):
        time   state space    geometrical space
 PDE      C          C              C
 ODE      C          C              D
 CML      D          C              D
 CA       D          D              D

   Perhaps the most famous CA is Conway's game "life." This CA evolves 
according to a deterministic rule which gives the state of a site in the next 
generation as a function of the states of neighboring sites in the present 
generation. This rule is applied to all sites.

For further reading see

   S. Wolfram (1986) Theory and Application of Cellular Automata, World 
Scientific Singapore.
   Physica 10D (1984)--the entire volume

Some programs that do CA, as well as more generally "artificial life" are 
available at

[3.17] What is a Bifurcation?
(Thanks to Zhen Mei for Contributions to this answer)

A bifurcation is a qualitative change in dynamics upon a small variation in 
the parameters of a system.

Many dynamical systems depend on parameters, e.g. Reynolds number, catalyst 
density, temperature, etc. Normally a gradually variation of a parameter in 
the system corresponds to the gradual variation of the solutions of the 
problem. However, there exists a large number of problems for which the number 
of solutions changes abruptly and the structure of solution manifolds varies 
dramatically when a parameter passes through some critical values. For 
example, the abrupt buckling of a stab when the stress is increased beyond a 
critical value, the onset of convection and turbulence when the flow 
parameters are changed, the formation of patterns in certain PDE's, etc. This 
kind of phenomena is called bifurcation, i.e. a qualitative change in the 
behavior of solutions of a dynamics system, a partial differential equation or 
a delay differential equation.

Bifurcation theory is a method for studying how solutions of a nonlinear 
problem and their stability change as the parameters varies. The onset of 
chaos is often studied by bifurcation theory. For example, in certain 
parameterized families of one dimensional maps, chaos occurs by infinitely 
many period doubling bifurcations

There are a number of well constructed computer tools for studying 
bifurcations. In particular see [5.2] for AUTO and DStool.

[3.18] What is a Hamiltonian Chaos?

The transition to chaos for a Hamiltonian (conservative) system is somewhat 
different than that for a dissipative system (recall [2.5]). In an integrable 
(nonchaotic) Hamiltonian system, the motion is "quasiperiodic", that is motion 
that is oscillatory, but involves more than one independent frequency (see 
also [2.12]). Geometrically the orbits move on tori, i.e. the mathematical 
generalization of a donut. Examples of integrable Hamiltonian systems include 
harmonic oscillators (simple mass on a spring, or systems of coupled linear 
springs), the pendulum, certain special tops (for example the Euler and 
Lagrange tops), and the Kepler motion of one planet around the sun. 

It was expected that a typical perturbation of an integrable Hamiltonian 
system would lead to "ergodic" motion, a weak version of chaos in which all of 
phase space is covered, but the Lyapunov exponents [2.11] are not necessarily 
positive. That this was not true was rather surprisingly discovered by one of 
the first computer experiments in dynamics, that of Fermi, Pasta and Ulam. 
They showed that trajectories in nonintegrable system may also be surprisingly 
stable. Mathematically this was shown to be the case by the celebrated theorem 
of Kolmogorov Arnold and Moser (KAM), first proposed by Kolmogorov in 1954. 
The KAM theorem is rather technical, but in essence says that many of the 
quasiperiodic motions are preserved under perturbations. These orbits fill out 
what are called KAM tori.

An amazing extension of this result was started with the work of John Greene 
in 1968. He showed that if one continues to perturb a KAM torus, it reaches a 
stage where the nearby phase space [2.4] becomes self-similar (has fractal 
structure [3.2]). At this point the torus is "critical," and any increase in 
the perturbation destroys it. In a remarkable sequence of papers, Aubry and 
Mather showed that there are still quasiperiodic orbits that exist beyond this 
point, but instead of tori they cover cantor sets [3.5]. Percival actually 
discovered these for an example in 1979 and named them "cantori." 
Mathematicians tend to call them "Aubry-Mather" sets. These play an important 
role in limiting the rate of transport through chaotic regions.

Thus, the transition to chaos in Hamiltonian systems can be thought of as the 
destruction of invariant tori, and the creation of cantori. Chirikov was the 
first to realize that this transition to "global chaos" was an important 
physical phenomena. Local chaos also occurs in Hamiltonian systems (in the 
regions between the KAM tori), and is caused by the intersection of stable and 
unstable manifolds in what Poincaré called the "homoclinic trellis."

To learn more: See the introductory article by Berry, the text by Percival and 
Richards and the collection of articles on Hamiltonian systems by MacKay and 
Meiss [4.1]. There are a number of excellent advanced texts on Hamiltonian 
dynamics, some of which are listed in [4.1], but we also mention

   Meyer, K. R. and G. R. Hall (1992), Introduction to Hamiltonian dynamical 
systems and the N-body problem  (New York, Springer-Verlag).

[4]   To Learn More
[4.1] What should I read to learn more?
1  Gleick, J. (1987). Chaos, the Making of a New Science. 
      London, Heinemann.
2  Stewart, I. (1989). Does God Play Dice? Cambridge, Blackwell.
3  Devaney, R. L. (1990). Chaos, Fractals, and Dynamics: Computer 
      Experiments in Mathematics. Menlo Park, Addison-Wesley
4  Lorenz, E., (1994) The Essence of Chaos, Univ. of Washington Press.
5  Schroeder, M. (1991) Fractals, Chaos, Power: Minutes from an infinite paradise
      W. H. Freeman New York: 
   Introductory Texts
1  Abraham, R. H. and C. D. Shaw (1992) Dynamics: The Geometry of 
      Behavior, 2nd ed. Redwood City, Addison-Wesley.
2  Baker, G. L. and J. P. Gollub (1990). Chaotic Dynamics. 
      Cambridge, Cambridge Univ. Press.
3  DevaneyDevaney, R. L. (1986). An Introduction to Chaotic Dynamical 
      Systems. Menlo Park, Benjamin/Cummings.
4  Kaplan, D. and L. Glass (1995). Understanding Nonlinear Dynamics, 
      Springer-Verlag New York.
5  Glendinning, P. (1994). Stability, Instability and Chaos. 
      Cambridge, Cambridge Univ Press. 
6  Jurgens, H., H.-O. Peitgen, et al. (1993). Chaos and Fractals: New 
      Frontiers of Science. New York, Springer Verlag.
7  Moon, F. C. (1992). Chaotic and Fractal Dynamics. New York, John Wiley. 
8  Percival, I. C. and D. Richard (1982). Introduction to Dynamics. Cambridge, 
      Cambridge Univ. Press. 
9  Scott, A. (1999). NONLINEAR SCIENCE: Emergence and Dynamics of 
      Coherent Structures, Oxford 
10 Smith, P (1998) Explaining Chaos, Cambridge
11 Strogatz, S. (1994). Nonlinear Dynamics and Chaos. Reading, 
12 Thompson, J. M. T. and H. B. Stewart (1986) Nonlinear Dynamics and 
      Chaos. Chichester, John Wiley and Sons.
13 Tufillaro, N., T. Abbott, et al. (1992). An Experimental Approach 
      to Nonlinear Dynamics and Chaos. Redwood City, Addison-Wesley.
14 Turcotte, Donald L. (1992). Fractals and Chaos in Geology and 
      Geophysics, Cambridge Univ. Press. 

   Introductory Articles
1  May, R. M. (1986). "When Two and Two Do Not Make Four." 
      Proc. Royal Soc. B228: 241.
2  Berry, M. V. (1981). "Regularity and Chaos in Classical Mechanics, 
      Illustrated by Three Deformations of a Circular Billiard." 
      Eur. J. Phys. 2: 91-102.
3  Crawford, J. D. (1991). "Introduction to Bifurcation Theory." 
      Reviews of Modern Physics 63(4): 991-1038.
3  Shinbrot, T., C. Grebogi, et al. (1992). "Chaos in a Double Pendulum." 
      Am. J. Phys 60: 491-499.
5  David Ruelle. (1980). "Strange Attractors," 
      The Mathematical Intelligencer 2:  126-37.

   Advanced Texts
1  Arnold, V. I. (1978). Mathematical Methods of Classical Mechanics.
     New York, Springer.
2  Arrowsmith, D. K. and C. M. Place (1990),  An Introduction to Dynamical Systems.
      Cambridge, Cambridge University Press.
3  Guckenheimer, J. and P. Holmes (1983), Nonlinear Oscillations, Dynamical
      Systems, and Bifurcation of Vector Fields, Springer-Verlag New York.
4  Kantz, H., and T. Schreiber (1997). Nonlinear time series analysis.
      Cambridge, Cambridge University Press
5  Katok, A. and B. Hasselblatt (1995), Introduction to the Modern
      Theory of Dynamical Systems, Cambridge, Cambridge Univ. Press. 
6  Hilborn, R. (1994), Chaos and Nonlinear Dyanamics: an Introduction for
      Scientists and Engineers, Oxford Univesity Press.
7  Lichtenberg, A.J. and M. A. Lieberman (1983), Regular and Chaotic Motion, 
      Springer-Verlag, New York .
8  Lind, D. and Marcus, B.  (1995) An Introduction to Symbolic Dynamics and 
       Coding, Cambridge University Press, Cambridge
9  MacKay, R.S and  J.D. Meiss  (eds) (1987), Hamiltonian Dynamical Systems 
      A reprint selection, , Adam Hilger, Bristol
10 Nayfeh, A.H.  and B. Balachandran (1995), Applied Nonlinear Dynamics:
      Analytical, Computational and Experimental Methods
      John Wiley& Sons Inc., New York
11 Ott, E. (1993). Chaos in Dynamical Systems. Cambridge University Press, 
12 L.E. Reichl, (1992), The Transition to Chaos, in Conservative and 
      Classical Systems: Quantum Manifestations  Springer-Verlag, New York
13 Robinson, C. (1999), Dynamical Systems: Stability, Symbolic
      Dynamics, and Chaos, 2nd Edition, Boca Raton, CRC Press.
14 Ruelle, D. (1989), Elements of Differentiable Dynamics and Bifurcation 
    Theory, Academic Press Inc.
15 Tabor, M. (1989), Chaos and Integrability in Nonlinear Dynamics:
      an Introduction, Wiley, New York.
16 Wiggins, S. (1990), Introduction to Applied Nonlinear Dynamical Systems 
and Chaos, Springer-Verlag New York.
17 Wiggins, S. (1988), Global Bifurcations and Chaos, Springer-Verlag New 

[4.2] What technical journals have nonlinear science articles?

Physica D                    The premier journal in Applied Nonlinear Dynamics
Nonlinearity                 Good mix, with a mathematical bias
Chaos                        AIP Journal, with a good physical bent
SIAM J. of Dynamical Systems Online Journal with multimedia
Chaos Solitons and Fractals  Low quality, some good applications
Communications in Math Phys  an occasional paper on dynamics
Comm. in Nonlinear Sci.      New Elsevier journal
     and Num. Sim.    
Ergodic Theory and           Rigorous mathematics, and careful work
      Dynamical Systems
International J of           lots of color pictures, variable quality.
    Bifurcation and Chaos
J Differential Equations     A premier journal, but very mathematical
J Dynamics and Diff. Eq.     Good, more focused version of the above
J Dynamics and Stability     Focused on Eng. applications. New editorial
      of Systems              board--stay tuned.
J Fluid Mechanics            Some expt. papers, e.g. transition to turbulence
J Nonlinear Science          a newer journal--haven't read enough yet.
J Statistical Physics        Used to contain seminal dynamical systems papers
Nonlinear Dynamics           Haven't read enough to form an opinion
Nonlinear Science Today      Weekly News:
Nonlinear Processes in       New, variable quality...may be improving
Physics Letters A            Has a good nonlinear science section
Physical Review E            Lots of Physics articles with nonlinear emphasis
Regular and Chaotic Dynamics Russian Journal

[4.3] What are net sites for nonlinear science materials?

   Bibliography Mainz http site Mainz ftp site Seach the Mainz Site Maryland  Complexity Bibliography Ergodic Theory and Dynamical Systems Nonlinear Dynamics Resources (pdf file) Sanjuan's Bibliography

   Preprint Archives  StonyBrook Los Alamos Preprint Server  Nonlinear Science Eprint Server   Math-Physics Archive AMS Preprint Servers List

   Conference Announcements Mathematics Conference List 
   StonyBrook List Munich List Los Alamos List Theoretical & Applied Mechanics SIAM Dynamical Systems 1999 

   gopher://   SIAM Dynamical Systems Group  UK Nonlinear News

   Education Sites  Devaney's Dynamical Systems Project

   Electronic Journals   Nonlinear Science Today Complexity  Complexity International Journal

   Electronic Texts An experimental approach 
            to nonlinear dynamics and chaos  Lecture Notes on Periodic Orbits The Chaos HyperTextBook

   Institutes and Academic Programs Physics Institutes   Nonlinear Groups Research Groups in Chaos

   Java Applets Sites Virtual Laboratory Java Pendulum 
     Java Fractal Explorer B. Fraser¹s Nonlinear Lab Mike Cross' Demos

   Who is Who in Nonlinear Dynamics Munich List  Stonybrook List

   Lists of Nonlinear sites Netscape¹s List  Tufillaro's List Peckham's List Physics Encyclopedia  Osinga's Software List

   Dynamical Systems  Dynamical Systems Home Page  Entropy and Dynamics

   Chaos sites  Chaos Metalink  Iterated Function Systems Playground Xah Lee's dynamics and Fractals pages Tutorial on Control of Chaos,,2090,00.html
             All about  Feigenbaum Constants The Feigenbaum Fractal  Mike Rosenstein's Chaos Page. Chaos in Psychology  Movies and Demonstrations

   Time Series Dynamics and Time Series Time series Analysis 
                 Time Series Data Library

   Complex Systems Sites  Complexity Home Page The Complexity & Artificial Life Web Site  Complexity and Physiology Site

   Fractals Sites A Fractal Gallery  The Spanky Fractal DataBase  Sprott's Fractal Gallery Projet Fractales  Lau's Fractal Stuff 3D Fractals  Fractal Gallery Fractal Domains Gallery Fractal Programs
         Fractal Programs

[5]   Computational Resources

[5.1] What are general computational resources?
   CAIN Europe Archives  Software Area
   FAQ guide to packages from sci.math.num-analysis
   NIST Guide to Available Mathematical Software
   Mathematics Archives Software
   Matpack, C++ numerical methods and data analysis library
   Numerical Recipes Home Page

[5.2] Where can I find specialized programs for  nonlinear 

   The Academic Software Library:
   Chaos Simulations
Bessoir, T., and A. Wolf, 1990. Demonstrates logistic map, Lyapunov exponents, 
billiards in a stadium, sensitive dependence, n-body gravitational motion.
   Chaos Data Analyser
A PC program for analyzing time series. By Sprott, J.C. and G. Rowlands. 
For more info:
   Chaos Demonstrations
A PC program for demonstrating chaos, fractals, cellular automata, and related 
nonlinear phenomena.  By J. C. Sprott and G. Rowlands.
System: IBM PC or compatible with at least 512K of memory.
Available: The Academic Software Library, (800) 955-TASL. $70.
   Chaotic Dynamics Workbench
Performs interactive numerical experiments on systems modeled by ordinary 
differential equations, including: four versions of driven Duffing 
oscillators, pendulum, Lorenz, driven Van der Pol osc., driven Brusselator, 
and the Henon-Heils system.  By R. Rollins.
System: IBM PC or compatible, 512 KB memory.
Available: The Academic Software Library, (800) 955-TASL, $70

   Applied Chaos Tools
Software package for time series analysis based on the UCSD group's, work. 
This package is a companion for Abarbanel's book Analysis of Observed Chaotic 
Data, Springer-Verlag.
System: Unix-Motif, Windows 95/NT
For more info see:

Bifurcation/Continuation Software (THE standard). The latest version is 
AUTO97. The GUI requires X and Motif to be present. There is also a command 
line version AUTO86. The software is transported as a compressed file called 
System: versions to run under X windows--SUN or sgi or LINUX
Available: anonymous ftp from 

Models Belousov- Zhabotinsky reaction based on the scheme of Ruoff and Noyes. 
The dynamics ranges from simple quasisinusoidal oscillations to quasiperiodic, 
bursting, complex periodic and chaotic.
System: DOS 6 and higher + PMODE/W DOS Extender. Also openGL version

Visual simulation in two- and three-dimensional phase space; based on visual 
algorithms rather than canned numerical algorithms; well-suited for 
educational use; comes with tutorial exercises. By Bruce Stewart
System: Silicon Graphics workstations, IBM RISC workstations with GL

A Program Collection for the PC by Korsch, H.J. and H-J. Jodl, 1994, A 
book/disk combo that gives a hands-on, computer experiment approach to 
learning nonlinear dynamics. Some of the modules cover billiard systems, 
double pendulum, Duffing oscillator, 1D iterative maps, an "electronic chaos-
generator", the Mandelbrot set, and ODEs.
System: IBM PC or compatible.
Available: $$

Chaos Programs to go with Baker, G. L. and J. P. Gollub (1990) Chaotic 
Dynamics. Cambridge, Cambridge Univ.
System: IBM, 512K memory, CGA or EGA graphics, True Basic
For more info: contact Gregory Baker, P.O. Box 278 ,Bryn Athyn, PA, 19009

   Chaos Analyser
Programs to Time delay embedding, Attractor (3d) viewing and animation, 
Poincaré sections, Mutual information, Singular Value Decomposition embedding, 
Full Lyapunov spectra (with noise cancellation), Local SVD analysis (for 
determining the systems dimension). By Mike Banbrook.
System: Unix, X windows
For more info:

   Chaos Cookbook
These programs go with J. Pritchard's book, The Chaos Cookbook System: 
Programs written in Visual Basic & Turbo Pascal
Available: $$

   Chaos Plot
ChaosPlot is a simple program which plots the chaotic behavior of a damped, 
driven anharmonic oscillator.
System: Macintosh
For more info:

   Cubic Oscillator Explorer
The CUBIC OSCILLATOR EXPLORER is a Macintosh application which allows 
interactive exploration of the chaotic processes of the Cubic Oscillator, 
i.e..Duffing's equation.
System: Macintosh + Digidesign DSP card, Digisystem init 2.6 and (optional) 
MIDI Manager
Available: (Missing??) Fractal Music

Signal and time series analysis package. Contains standard facilities for 
signal processing as well as advanced features like wavelet techniques and 
methods of nonlinear dynamics.
Systems: MS Windows, Linux, SUN Solaris 2.6
Available: $$

Free software from Guckenheimer's group at Cornell; DSTool has lots of 
examples of chaotic systems, Poincaré sections, bifurcation diagrams.
System: Unix, X windows.

   Dynamical Software Pro
Analyze non-linear dynamics and chaos. Includes ODEs, delay differential 
equations, discrete maps, numerical integration, time series embedding, etc. 
System: DOS. Microsoft Fortran compiler for user defined equations.
Available: SciTech

   Dynamics: Numerical Explorations.
A book + disk by H. Nusse,  and J.Yorke. A hands on approach to learning the 
concepts and the many aspects in computing relevant quantities in chaos
System: PC-compatible computer or X-windows system on Unix computers
Available: $$ 

   Dynamics Solver
Dynamics Solver solve numerically both initial-value problems and boundary-
value problems for continuous and discrete dynamical systems.
System: Windows 3.1 or Windows 95/98/NT

Phase plane portraits of 2D ODEs by Etienne Dupuis
System: Windows 95/98
Available: (Missing??)

A program to estimate fractal dimensions of a set. By DiFalco/Sarraille  
System: C source code, suitable for compiling for use on a Unix or DOS 

FracGen is a freeware program  to create fractal images using Iterated 
Function Systems. A tutorial is provided with the program. By Patrick Bangert  
System: PC-compatible computer, Windows 3.1

   Fractal Domains
Generates of Mandelbrot and Julia sets. By Dennis C. De Mars
System: Power Macintosh

   Fractal Explorer
Generates Mandelbrot and Newton's method fractals. By Peter Stone
System: Power Macintosh

   GNU Plotutils
The GNU plotutils package contains C/C++ function library for exporting 2-D 
vector graphics in many file formats, and for doing vector graphics 
animations. The package also contains several command-line programs for 
plotting scientific data, such as GNU graph, which is based on libplot, and 
ODE integration software.
System: GNU/Linux, FreeBSD, and Unix systems.

A program to visually study a reaction-diffusion model based on the 
Brusselator from Future Skills Software, Herber Sauro.
System: Requires Windows 95, at least 256 colours
Available :

(It's a Nonlinear Systems Investigative Toolkit for Everyone) is a collection 
for the simulation and characterization of dynamical systems, with an emphasis 
on chaotic systems. Companion software for T.S. Parker and L.O. Chua (1989) 
Practical Numerical Algorithms for Chaotic Systems  Springer Verlag. See their 
paper "INSITE A Software Toolkit for the Analysis of Nonlinear Dynamical 
Systems," Proc. of the IEEE, 75, 1081-1089 (1987).
System: C codes in Unix Tar or DOS format (later requires QuickWindowC
             or MetaWINDOW/Plus 3.7C. and  MS C compiler 5.1)
Available: INSITE SOFTWARE, p.o. Box 9662, Berkeley, CA , U.S.A.

   Institut fur ComputerGraphik
A collection of programs for developing advanced visualization techniques in 
the field of three-dimensional dynamical systems. By Löffelmann H., Gröller E.
System: various, requires AVS

A tool for studying one-dimensional (1D) discrete dynamical systems. Does 
bifurcation diagrams, etc. for a number of maps
System: PC compatible computer, DOS, VGA graphics

An interactive tool for bifurcation analysis of non-linear ordinary 
differential equations ODE's and maps. By Khibnik, Nikolaev, Kuznetsov and V. 
System: Now part of XPP (See below)

   Lyapunov Exponents
Keith Briggs Fortran codes for Lyapunov exponents
System: any with a Fortran compiler

   Lyapunov Exponents and Time Series
Based on Alan Wolf's algorithm, see [2.11], but a more efficient version.
System: Comes as C source, Fortran source, PC executable, etc
Available:  (Seems to be 

   Lyapunov Exponents and Time Series
Michael Banbrook's C codes for Lyapunov exponents & time series analysis
System: Sun with X windows.

   Lyapunov Exponents Toolbox (LET)
A user-contributed MATLAB toolbox  that provides a graphical user interface 
for users to determine the full sets of Lyapunov exponents and Lyapunov 
dimensions of discrete and continuous chaotic systems.
System: MATLAB 5

A Matlab program based on the QR Method , by von Bremen, Udwadia, and 
Proskurowski, Physica D, vol. 101, 1-16, (1997)
System: Matlab

   Macintosh Dynamics Programs
Lists available at:

Comes on a disk with the book MacMath, by Hubbard and West. A collection of 
programs for dynamical systems (1 & 2 D maps, 1 to 3D flows). Version 9.2 is 
the current version, but West is working on a much improved update.
System: Macintosh
For more info:
Available: $$ Springer-Verlag http://www.springer- 

Solves Differential and Difference Equations. Runs STELLA. Has a parser with a 
control language. By Robert Macey and George Oster at Berkeley
System: Macintosh or Windows 95 or later
Available : $$

   MatLab Chaos
A collection of routines for generate diagrams which illustrate chaotic 
behavior associated with the logistic equation.
System: Requires MatLab.
Available :

MTRCHAOS and MTRLYAP compute correlation dimension and largest Lyapunov 
exponents, delay portraits. By Mike Rosenstein. 
System: PC-compatible computer running DOS 3.1 or higher, 640K RAM, and EGA 
display. VGA & coprocessor recommended

   Nonlinear Dynamics Toolbox
Josh Reiss' NDT includes routines for the analysis of chaotic data, such as 
power spectral analyses, determination of the Lyapunov spectrum, mutual 
information function, prediction, noise reduction, and dimensional analysis.
System: Windows 95, 98, or NT
Available : Missing??

   NLD Toolbox
This toolbox has many of the standard dynamical systems, By Jeff Brush
System: PC, MS-DOS.

A program for integrating boundary value and initial value Problems for up to 
9th order ODEs. By Optimal Designs.
System: PC 386+, DOS 3.3+, 16 bit arch.
Available :

Kocak, H., 1989. Differential and Difference Equations through Computer 
Experiments: with a supplementary diskette containing PHASER: An 
Animator/Simulator for Dynamical Systems. Demonstrates a large number of 1D-4D 
differential equations--many not chaotic--and 1D-3D difference equations.
System: PC-compatible
Available: Springer-Verlag http://www.springer-

Software for physiologic signal processing and analysis, detection of 
physiologically significant events using both classical techniques and novel 
methods based on statistical physics and nonlinear dynamics
System: Unix

   Recurrence Quantification Analysis
Recurrence plots give a visual indication of deterministic behavior in complex 
time series. The program, by Webber and Zbilut creates the plots and 
quantifies the determinism with five measures.
System: DOS executable

A simulation program similar in intent to MatLab. It's primarily designed for 
systems/signals work, and is large. From INRIA in France.
System: Unix, X Windows, 20 Meg Disk space.
Available :

Iterates Area Preserving Maps, by J. D. Meiss. Iterates 8 different maps. It 
will find periodic orbits, cantori, stable and unstable manifolds, and allows 
you to iterate curves.
System: Macintosh

Simulates dynamics for Biological and Social systems modelling. Uses a 
building block metaphor constructing models.
System: Macintosh and Windows PC
Available: $$

   Time Series Tools
An extensive list of Unix tools for Time Series analysis
System: Unix
For more info: (Link 

   Time Series Analysis from Darmstadt
Four prgrams Time Series analysis and Dimension calculation from the Institute 
of Applied Physics at Darmstadt.
System: OS2 or Solaris/Linux/Win9X/NT + Fortran source
For more info:

   Time Series Analysis from Kennel
The program mkball  finds the minimum embedding dimension using the false 
strands enhancement of the false neighbors algorithm of Kennel & Abarbanel.
System: any C compiler

   TISEAN Time Series Analysis
Agorithms for data representation, prediction, noise reduction, dimension and 
Lyapunov estimation, and nonlinearity testing. By Rainer Hegger, Holger Kantz 
and Thomas Schreiber
System:  C, C++ and Fortran Codes for Unix,

   Tufillaro's Programs
From the book Nonlinear Dynamics and Chaos by Tufillaro, Abbot and Reilly 
(1992) (for a sample section see A collection of programs for 
the Macintosh.
System: Macintosh

   Unified Life Models (ULM)
ULM, by Stephane Legendre, is a program to study population dynamics and more 
generally, discrete dynamical systems. It models any species life cycle graph 
(matrix models) inter- and intra-specific competition (non linear systems), 
environmental stochasticity, demographic stochasticity (branching processes), 
and  metapopulations, migrations (coupled systems).
System: PC/Windows 3.X
Available: from

   Virtual Laboratory
Simulations of 2D active media by the Complex Systems Group at the Max Planck 
Inst. in Berlin.
System: Requires PV-Wave by Visual Numerics 
Available: $$

   VRA (Visual Recurrence Analysis)
VRA is a software to display and Study the recurrence plots, first described 
by Eckmann, Oliffson Kamphorst And Ruelle in 1987. With RP, one can 
graphically detect hidden patterns and structural changes in data or see 
similarities in patterns across the time series under study. By Eugene Kononov
Stystem: Windows 95

Phase 3D plane program for X-windows systems (for systems like Lorenz, 
Rossler). Plot, rotate in 3-d, Poincaré sections, etc. By Thomas P. Witelski
System: X-windows, Unix, SunOS 4 binary

Differential equations and maps for x-windows systems. Links to Auto for 
bifurcation analysis. By Bard Ermentrout
System: X-windows, Binaries for many unix systems
Available :

Simulate pattern formation in 2-D excitable media (in particular 2 models, one 
of them the  FitzHugh-Nagumo). By Flavio Fenton.
System: X-windows
Available : (Missing??)

[6] Acknowledgments

    Alan Champneys
    Jim Crutchfield chaos@gojira.Berkeley.EDU
    S. H. Doole
    David Elliot
    Fred Klingener
    Matt Kennel
    Jose Korneluk
    Wayne Hayes
    Justin Lipton
    Ronnie Mainieri
    Zhen Mei
    Gerard Middleton middleto@mcmail.CIS.McMaster.CA
    Andy de Paoli
    Lou Pecora
    Pavel Pokorny,
    Leon Poon
    Hawley Rising,
    Michael Rosenstein
    Harold Ruhl
    Troy Shinbrot
    Viorel Stancu
    Jaroslav Stark
    Bruce Stewart
    Richard Tasgal

Anyone else who would like to contribute, please do! Send me your comments: Jim Meiss at

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