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Next: Example: deterministic diffusion in Up: Deterministic Brownian motion Previous: Deterministic Brownian motion

Mathematical background

The classical theory of Brownian motion considers it as a stochastic process, i.e. as a motion driven by a random force that results from the superposition of a large number of independent collisions. Deterministic chaos has similar effects to a stochastic process, and motion driven by a force depending on single low-dimensional chaotic process can exhibit properties similar to those of stochastic Brownian motion, e.g. mean walking distance tex2html_wrap_inline838 , and is then called deterministic Brownian motion or deterministic diffusion. A well studied class of such processes, starting from the work [10], are chaotic and periodic iterated maps; there the possibility of macroscopic `diffusion' motion is provided by a noncompact discrete group of translations. Here we deal with a continuous symmetry group and continuous time, and will use a continuous version of corresponding statements:

Theorem 1 Let a semi-flow tex2html_wrap_inline840 in a Banach space tex2html_wrap_inline842

displaymath77

be ergodic with an invariant measure tex2html_wrap_inline844 , tex2html_wrap_inline846 . Suppose a function tex2html_wrap_inline848 has a zero mean value in tex2html_wrap_inline844 ,

displaymath83

and its autocorrelation function

  equation88

quickly decays; more precisely,

  equation95

Consider a point with coordinate tex2html_wrap_inline852 moving with the velocity V,

  equation104

Then the mean squared displacement of the point in a given time interval t,

displaymath107

grows linearly at large t,

  equation116

for almost all u with respect to tex2html_wrap_inline844 .

If a stronger condition on the autocorrelation function is fulfilled,

  equation119

then this estimation can be strengthened:

  equation126

We did not find a proof of this statement in literature, and so we present it in the Appendix.

We assume that the invariant measure, ergodicity and quickly decaying correlation function are consequences of a chaotic attractor of the semiflow tex2html_wrap_inline840 , though detailed specification of suitable attractors would lead us away from the main subject.

Naturally, in case of nonzero tex2html_wrap_inline866 , the mean square of the displacement grows as tex2html_wrap_inline868 , i.e. there is a directed drift with velocity tex2html_wrap_inline866 rather than Brownian walk. However, it is easy to see that if other conditions of the Theorem are fulfilled, in the frame of reference moving with velocity tex2html_wrap_inline866 we shall observe Brownian walk again. In other words, in general case there is a superposition of the directed and Brownian motions.


next up previous
Next: Example: deterministic diffusion in Up: Deterministic Brownian motion Previous: Deterministic Brownian motion

Vadim Biktashev
Tue Nov 25 16:48:21 GMT 1997