DYNAMICAL SYSTEMS THEORY
The term deterministic chaos indicates a strong sensitivity on
initial conditions, that is, exponential separation of nearby
trajectories in phase space.
In dissipative systems, when the temporal evolution is bounded in a
limited region of the phase space, a small volume should fold,
after an initial stretching due to the strong sensitivity on the
initial state.
In presence of chaos, the competitive effect of repeated
stretching and folding produces very complex and irregular
structures in phase space (see an example in
Transport and Diffusion).
The asymptotic motion evolves on a foliated structure called
a strange attractor, usually with non-integer Hausdorff
dimension.
In other words, strange attractors are often fractals.
In large systems, just as in small ones, the existence of a positive
Lyapunov exponent (LE) is the standard criterion for chaos.
In high dimensional systems besides the practical numerical difficulties
one has to face with additional problems, for instance the spatial correlation,
the existence of a thermodynamic limit for quantities as the whole
spectrum of the Lyapunov exponents and the dimension of the attractor.
However, a chaotic extended system can be coherent (i.e. spatially ordered)
or incoherent (spatially disordered).
Dynamical systems with many degrees of freedom may have
many time scales, somehow related to the different
scales of motion in the phase space.
In contrast with systems modeled in terms of random processes, such as
Langevin equations, it is not possible to separate the
degrees of freedom in only two classes, corresponding to the slow and
the fast modes.
In addition, even if the maximum Lyapunov exponent is negative, and
the system is not chaotic, one can have a sort of "spatial
complexity". This happens in the open flows in presence of the
convective instability.
Let us give some paradigmatic examples of real systems with
chaotic behavior:
- (A) Fluid-dynamical Turbulence
- (B) Chemical Turbulence. A celebrated example is the
Belousov-Zhabotinsky reaction in which one has
time dependence in the concentration, in the form of
limit cycles or even strange attractors.
- (C) Pattern formation, e.g. Turing structures.
- (D) Fronts dynamics, e.g. combustion.
This kind of phenomena can be studied in terms of dynamical systems as
- (A) Shell models
- (B) Partial Differential Equations, as Kuramoto-Sivashinky
- (C) Coupled Map Lattices (CML)
- (D) Cellular Automata (CA) and CML
In the characterization of the behaviors of dynamical systems
one is faced by two different cases:
- (a) the evolution laws are known
- (b) one has some time record from an experiment
and the relevant variables are not known.
In the case (a), at least at non rigorous level and with many
nontrivial exceptions, it is possible to give quantitative
characterizations in terms of Lyapunov exponents, dimension
of the attractor, Kolmogorov-Sinai entropy, and so on. In particular,
by means of these tools one can quantify the ability to make definite
predictions on the system, i.e. to give an answer to the so called
predictability problem.
The case (b), from a conceptual point of view, is quite similar to the
case (a). If one is able to reconstruct the phase space then the
computation of quantities as Lyapunov exponent and fractal dimension
can be performed basically with the same techniques of case (a).
On the other hand there are rather severe practical limitations for
not so high dimensional systems and even in low dimensional ones
non trivial features can appear in presence of noise.
Let us remark that the mathematically well defined basic concepts
(e.g. Lyapunov exponents and attractor dimension) in dynamical systems
refer only to asymptotic limits, i.e. infinite time and infinitesimal
perturbation. Therefore, in realistic systems, in which one typically
has to deal with non infinitesimal perturbations and finite times, it
is necessary to introduce suitable tools which do not involve these
limits.
The standard scenario for predictability in dynamical systems can be
summarized as follows. Based on the classical deterministic point of
view of Laplace [1814], it is in principle possible to predict the
state of a system, at any time, once the evolution laws and the
initial conditions are known. In practice, since the initial conditions
are not known with arbitrary precision, one considers a system
predictable just up to the time at which the uncertainty
reaches some threshold value D, determined by the particular needs.
In the presence of deterministic chaos,
because of the exponential divergence of the distance between two
initially close trajectories, an uncertainty Dx(0) on
the state of the system at time t=0 typically increases as
|Dx(t)| = |Dx(0)| exp(lambda t) (1)
where
lambda is the maximum Lyapunov exponent.
As a consequence,
starting with Dx(0)=d0,
the typical predictability time is
Tp= 1/lambda ln(D/d0). (2)
Basically, this relation shows that the predictability time is
proportional to the inverse of the Lyapunov exponent: its dependence
on the precision of the measure and the threshold, for practical
purposes, can be neglected.
Relation (2) is a satisfactory answer to the predictability
problem only for d0,D infinitesimal and for long times.
The above written simple link between predictability and maximum
Lyapunov exponent fails in generic settings of dynamical systems.
Let us briefly discuss why.
- The Lyapunov exponent lambda is a global quantity: it
measures the average exponential rate of divergence of
nearby trajectories. In general there exist finite-time
fluctuations of this rate and it is possible to define an
``instantaneous'' rate: the ``effective Lyapunov
exponent'' . For finite time delay tau, the
effective LE depends on the particular point of the
trajectory x(t) where the perturbation is performed.
In the same way, the predictability time
Tp fluctuates, following the variations of
the effective LE.
- In dynamical systems with
many degrees of freedom, the interactions among different
degrees of freedom play an important role in the growth of
the perturbation. If one is interested in the case of a
perturbation concentrated on certain degrees of freedom
(e.g. small length scales in weather forecasting), and a
prediction on the evolution of other degrees of freedom
(e.g. large length scales), even the knowledge of the
statistics of the effective Lyapunov exponent is not
sufficient. In this case it is important to understand the
behaviour of the tangent vector z(t), i.e. the
direction along which an infinitesimal perturbation
grows. In such a situation a relevant quantity can result
the time, TR, the tangent vector needs to
relax on the time dependent eigenvector e1(t) of
the stability matrix, corresponding to the maximum Lyapunov
exponent lambda1.
So that, in this context, one has:
T = TR+1/lambda ln(D/d0) (3)
and the mechanism of transfer of the error Dx
through the degrees of freedom of the system, which
determines TR,
could be more important than the rate of
divergence of nearby trajectories.
- In systems
with many characteristic times -- such as the eddy turn-over
times in fully developed turbulence
-- if one is interested
in non infinitesimal perturbations Tp
is determined by the
detailed process due to the nonlinear effects in the
evolution equation for Dx. In this case, the
predictability time could be unrelated to the maximum
Lyapunov exponent and Tp
might depend, in a non-trivial
way, on the details of the system. Therefore one needs a new
indicator, such as the finite size Lyapunov exponent (FSLE)
, to
characterize quantitatively the error growth of non
infinitesimal perturbations.
- In presence of
noise, or in general of probabilistic rules in the evolution
laws (e.g. random maps), there are two different ways to
define the predictability: by considering either two
trajectories of the system with the same noise or two
trajectories of the same system evolving with different
realizations of the noise. Both these definitions are
physically relevant in different contexts but the results
can be very different in presence of a strong dynamical
intermittency.
- In spatially extended systems
one can have both temporal and/or spatial complex
behaviour. In particular, even in absence of temporal chaos
(i.e. lambda <0) one can have irregularity in the
spatial features. Thus even if temporal sequences of a given
site are predictable the detailed spatial structure is very
``complex'' (unpredictable). In particular, in the so called
open flows (as shear flow downstream) convective instability
may occur, i.e. even if the Lyapunov exponent is negative
perturbations may grow in a coordinate system traveling
with non zero speed. From this the necessity to define new
indicators such as the co-moving Lyapunov exponent (CLE).
In such
situations one has the phenomenon of sensitivity on boundary
conditions which can be detected by a ``spatial Lyapunov
exponent''.
In the study of data sequences another approach, at first glance
completely different, has been developed in the context of the
information theory, data compression and algorithmic complexity theory.
Nowadays it is rather clear that this approach is
closely related to the dynamical systems one. Basically, if a system is
chaotic, i.e. there is strong sensitivity on the initial conditions, and
the predictability is limited up to a time which is related to the
first Lyapunov exponent, then a time sequence obtained from one of its
chaotic trajectories cannot be compressed by an arbitrary factor.
It is easy to give an interpretation of eq. (2) in terms of
cost of the transmission, or difficulty in the compression,
of a record x(1),x(2),......,x(N). For instance,
in the discrete-time case with a unique positive Lyapunov exponent,
one can show that, in the limit N--->infty, the minimum number of
bits per unit time necessary to transmit the sequence is lambda/ln2.
This is a rephrasing, in the context of the dynamical systems,
of the theorem for the maximum compressibility which, in information theory,
is stated in terms of the Shannon entropy.
On the other hand, as for the basic theoretical concepts introduced
in dynamical systems theory, also in this context, in order to treat
realistic problems, it is necessary to extend and generalize
the fundamental notions of the information and data compression theory.
In this framework perhaps the most important development has been
the idea of epsilon- entropy (or rate distortion function, according
to Shannon) which is the information counterpart of the finite size
Lyapunov exponent.
The study of the predictability, a part its obvious interest per se
and for applications (e.g. in geophysics and astronomy), can be read,
from a conceptual point of view, as a way to characterize the
``complexity'' of dynamical systems.