scholarly journals Energy and Enstrophy Spectra of Geostrophic Turbulent Flows Derived from a Maximum Entropy Principle

2009 ◽  
Vol 66 (8) ◽  
pp. 2216-2236 ◽  
Author(s):  
W. T. M. Verkley ◽  
Peter Lynch

Abstract The principle of maximum entropy is used to obtain energy and enstrophy spectra as well as average relative vorticity fields in the context of geostrophic turbulence on a rotating sphere. In the unforced-undamped (inviscid) case, the maximization of entropy is constrained by the constant energy and enstrophy of the system, leading to the familiar results of absolute statistical equilibrium. In the damped (freely decaying) and forced-damped case, the maximization of entropy is constrained by either the decay rates of energy and enstrophy or by the energy and enstrophy in combination with their decay rates. Integrations with a numerical spectral model are used to check the theoretical results for the different cases. Maximizing the entropy, constrained by the energy and enstrophy, gives a good description of the energy and enstrophy spectra in the inviscid case, in accordance with known results. In the freely decaying case, not too long after the damping has set in, good descriptions of the energy and enstrophy spectra are obtained if the entropy is maximized, constrained by the energy and enstrophy in combination with their decay rates. Maximizing the entropy, constrained by the energy and enstrophy in combination with their (zero) decay rates, gives a reasonable description of the spectra in the forced-damped case, although discrepancies remain here.

1990 ◽  
Vol 27 (2) ◽  
pp. 303-313 ◽  
Author(s):  
Claudine Robert

The maximum entropy principle is used to model uncertainty by a maximum entropy distribution, subject to some appropriate linear constraints. We give an entropy concentration theorem (whose demonstration is based on large deviation techniques) which is a mathematical justification of this statistical modelling principle. Then we indicate how it can be used in artificial intelligence, and how relevant prior knowledge is provided by some classical descriptive statistical methods. It appears furthermore that the maximum entropy principle yields to a natural binding between descriptive methods and some statistical structures.


Author(s):  
KAI YAO ◽  
JINWU GAO ◽  
WEI DAI

Entropy is a measure of the uncertainty associated with a variable whose value cannot be exactly predicated. In uncertainty theory, it has been quantified so far by logarithmic entropy. However, logarithmic entropy sometimes fails to measure the uncertainty. This paper will propose another type of entropy named sine entropy as a supplement, and explore its properties. After that, the maximum entropy principle will be introduced, and the arc-cosine distributed variables will be proved to have the maximum sine entropy with given expected value and variance.


Sign in / Sign up

Export Citation Format

Share Document