Statistical Mechanics and Information Theory for Complex Systems

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Information Theory ◽  
Statistical Mechanics ◽  
Complex Systems ◽  
Maximum Entropy Principle ◽  
Distribution Functions ◽  
Complex Processes ◽  
Entropy Principle ◽  
Path Dependent ◽  
Statistical Systems ◽  
Dependent Processes

Most complex systems are statistical systems. Statsitical mechanics and information theory usually do not apply to complex systems because the latter break the assumptions of ergodicity, independence, and multinomial statistics. We show that it is possible to generalize the frameworks of statistical mechanics and information theory in a meaningful way, such that they become useful for understanding the statistics of complex systems.We clarify that the notion of entropy for complex systems is strongly dependent on the context where it is used, and differs if it is used as an extensive quantity, a measure of information, or as a tool for statistical inference. We show this explicitly for simple path-dependent complex processes such as Polya urn processes, and sample space reducing processes.We also show it is possible to generalize the maximum entropy principle to path-dependent processes and how this can be used to compute timedependent distribution functions of history dependent processes.

The Evolution of Complex Systems

1988 ◽  
Vol 43 (1) ◽  
pp. 73-77
Author(s):  
G. L. Hofacker ◽  
R. D. Levine
Keyword(s):  
Complex Systems ◽  
Maximum Entropy ◽  
Transition Probabilities ◽  
Maximum Entropy Principle ◽  
Average Energy ◽  
Entropy Principle ◽  
Fundamental Equations ◽  
Special Case ◽  
Extremal Properties

Abstract A principle of evolution of highly complex systems is proposed. It is based on extremal properties of the information I (X, Y) characterizing two states X and Y with respect to each other, I(X, Y) = H(Y) -H(Y/X), where H(Y) is the entropy of state Y,H (Y/X) the entropy in state Y given the probability distribu­tion P(X) and transition probabilities P(Y/X).As I(X, Y) is maximal in P(Y) but minimal in P(Y/X), the extremal properties of I(X, Y) con­stitute a principle superior to the maximum entropy principle while containing the latter as a special case. The principle applies to complex systems evolving with time where fundamental equations are unknown or too difficult to solve. For the case of a system evolving from X to Y it is shown that the principle predicts a canonic distribution for a state Y with a fixed average energy .

A hydrodynamic model for silicon semiconductors including crystal heating

2015 ◽  
Vol 26 (4) ◽  
pp. 477-496 ◽  
Author(s):  
GIOVANNI MASCALI
Keyword(s):  
Transition Rate ◽  
Evolution Equations ◽  
Kinetic Equations ◽  
Maximum Entropy Principle ◽  
Distribution Functions ◽  
Macroscopic Model ◽  
State Variables ◽  
Silicon Devices ◽  
Electron Distributions ◽  
Entropy Principle

We present a macroscopic model for describing the electrical and thermal behaviour of silicon devices. The model makes use of a set of macroscopic state variables for phonons and electrons that are moments of their respective distribution functions. The evolution equations for these variables are obtained starting from the Bloch–Boltzmann–Peierls kinetic equations for the phonon and the electron distributions, and are closed by means of the maximum entropy principle. All the main interactions between electrons and phonons, the scattering of electrons with impurities, as well as the scattering of phonons among themselves are considered. In particular, we propose a treatment of the optical phonon decay directly based on the expression of its transition rate (Klemens 1966Phys. Rev.148 845; Aksamija & Ravaioli 2010Appl. Phys. Lett.96, 091911). As an application of the model, we evaluate the silicon thermopower.

A new stochastic analysis method for mechanical components

2012 ◽  
Vol 227 (8) ◽  
pp. 1818-1829 ◽  
Author(s):  
YL Zhang ◽  
YM Zhang
Keyword(s):  
Finite Element ◽  
Dimension Reduction ◽  
Probability Density ◽  
Maximum Entropy ◽  
Maximum Entropy Principle ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Entropy Principle ◽  
Input Variables ◽  
First Four Moments

Univariate dimension-reduction integration, maximum entropy principle, and finite element method are employed to present a computational procedure for estimating probability densities and distributions of stochastic responses of structures. The proposed procedure can be described as follows: 1. Choose input variables and corresponding distributions. 2. Calculate the integration points and perform finite element analysis. 3. Calculate the first four moments of structural responses by univariate dimension-reduction integration. 4. Estimate probability density function and cumulative distribution function of responses by maximum entropy principle. Numerical integration formulas are obtained for non-normal distributions. The non-normal input variables need not to be transformed into equivalent normal ones. Three numerical examples involving explicit performance functions and solid mechanic problems without explicit performance functions are used to illustrate the proposed procedure. Accuracy and efficiency of the proposed procedure are demonstrated by comparisons of the estimated probability density functions and cumulative distribution functions obtained by maximum entropy principle and Monte Carlo simulation.

FROM THERMODYNAMICS TO MAXENT

2007 ◽  
Vol 21 (13n14) ◽  
pp. 2557-2563
Author(s):  
A. PLASTINO ◽  
E. M. F. CURADO
Keyword(s):  
Statistical Mechanics ◽  
Maximum Entropy ◽  
Body Problem ◽  
Maximum Entropy Principle ◽  
Many Body ◽  
Laws Of Thermodynamics ◽  
Entropy Principle

The maximum entropy principle (MaxEnt) is one of the most powerful approaches in the theorist arsenal. It has been used in many areas of science, and, in particular, in both statistical mechanics and the quantum many body problem. Here we show how to derive MaxEnt from the laws of thermodynamics.

A PROPER NONLOCAL FORMULATION OF QUANTUM MAXIMUM ENTROPY PRINCIPLE IN STATISTICAL MECHANICS

2012 ◽  
Vol 26 (12) ◽  
pp. 1241007 ◽  
Author(s):  
M. TROVATO ◽  
L. REGGIANI
Keyword(s):  
Statistical Mechanics ◽  
Maximum Entropy ◽  
Maximum Entropy Principle ◽  
Quantum Statistical Mechanics ◽  
Fundamental Principle ◽  
Quantum Entropy ◽  
Closure Condition ◽  
Entropy Principle ◽  
Quantum Hydrodynamic ◽  
Reduced Density

By considering Wigner formalism, the quantum maximum entropy principle (QMEP) is here asserted as the fundamental principle of quantum statistical mechanics when it becomes necessary to treat systems in partially specified quantum states. From one hand, the main difficulty in QMEP is to define an appropriate quantum entropy that explicitly incorporates quantum statistics. From another hand, the availability of rigorous quantum hydrodynamic (QHD) models is a demanding issue for a variety of quantum systems. Relevant results of the present approach are: (i) The development of a generalized three-dimensional Wigner equation. (ii) The construction of extended quantum hydrodynamic models evaluated exactly to all orders of the reduced Planck constant ℏ. (iii) The definition of a generalized quantum entropy as global functional of the reduced density matrix. (iv) The formulation of a proper nonlocal QMEP obtained by determining an explicit functional form of the reduced density operator, which requires the consistent introduction of nonlocal quantum Lagrange multipliers. (v) The development of a quantum-closure procedure that includes nonlocal statistical effects in the corresponding quantum hydrodynamic system. (vi) The development of a closure condition for a set of relevant quantum regimes of Fermi and Bose gases both in thermodynamic equilibrium and nonequilibrium conditions.

scholarly journals Derivation of some new distributions in statistical mechanics using maximum entropy approach

2014 ◽  
Vol 24 (1) ◽  
pp. 145-155 ◽  
Author(s):  
Amritansu Ray ◽  
S.K. Majumder
Keyword(s):  
Statistical Mechanics ◽  
Maximum Entropy ◽  
Maximum Entropy Principle ◽  
The Other ◽  
Central Idea ◽  
Entropy Principle ◽  
Bose Einstein ◽  
The Way

The maximum entropy principle has been earlier used to derive the Bose Einstein(B.E.), Fermi Dirac(F.D.) & Intermediate Statistics(I.S.) distribution of statistical mechanics. The central idea of these distributions is to predict the distribution of the microstates, which are the particle of the system, on the basis of the knowledge of some macroscopic data. The latter information is specified in the form of some simple moment constraints. One distribution differs from the other in the way in which the constraints are specified. In the present paper, we have derived some new distributions similar to B.E., F.D. distributions of statistical mechanics by using maximum entropy principle. Some proofs of B.E. & F.D. distributions are shown, and at the end some new results are discussed.

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