scholarly journals Some Predictions from the Mean Field Equations of Magnetoconvection

1980 ◽  
Vol 33 (1) ◽  
pp. 47 ◽  
Author(s):  
N Riahi
Keyword(s):  
Mean Field ◽  
Field Approximation ◽  
Mean Field Approximation ◽  
Field Equations ◽  
Layer Method ◽  
Mean Field Equations ◽  
Boundary Layer Method ◽  
The Mean ◽  
Chandrasekhar Number ◽  
Large Rayleigh Number

Nonlinear magnetic convection is investigated by the mean field approximation. The boundary layer method is used assuming large Rayleigh number R for different ranges of the Chandrasekhar number Q. The heat flux F is determined for wavenumbers CXn which optimize F. It is shown that there are a finite number of modes in the ranges Q ~ R2/3 and R2/3 ~ Q ~ R, and that the number of modes increases with increasing Q in the former range and decreases with increasing Q in the latter range. For Q = 0(R2/3) there are infinitely many modes, and F is proportional to Rl/3 While the optimal F is independent of Q for Q ~ Rl/2, it is found to decrease with increasing Q in the range Rl/2 ~ Q ~ R and eventually to become of 0(1) as Q -> OCR), and the layer becomes stable.

Attractor Dynamics in Feedforward Neural Networks

2000 ◽  
Vol 12 (6) ◽  
pp. 1313-1335 ◽  
Author(s):  
Lawrence K. Saul ◽  
Michael I. Jordan
Keyword(s):  
Neural Networks ◽  
Mean Field ◽  
Feedforward Neural Networks ◽  
Field Approximation ◽  
Generative Models ◽  
Mean Field Approximation ◽  
Field Equations ◽  
Attractor Dynamics ◽  
Mean Field Equations ◽  
The Mean

We study the probabilistic generative models parameterized by feedfor-ward neural networks. An attractor dynamics for probabilistic inference in these models is derived from a mean field approximation for large, layered sigmoidal networks. Fixed points of the dynamics correspond to solutions of the mean field equations, which relate the statistics of each unittothoseofits Markovblanket. We establish global convergence of the dynamics by providing a Lyapunov function and show that the dynamics generate the signals required for unsupervised learning. Our results for feedforward networks provide a counterpart to those of Cohen-Grossberg and Hopfield for symmetric networks.

Refining Mean-field Approximations by Dynamic State Truncation

2021 ◽  
Vol 5 (2) ◽  
pp. 1-30
Author(s):  
Francesca Randone ◽  
Luca Bortolussi ◽  
Mirco Tribastone
Keyword(s):  
State Space ◽  
Mean Field ◽  
Field Approximation ◽  
The State ◽  
Mean Field Approximation ◽  
Dynamic State ◽  
Field Equations ◽  
Mean Field Equations ◽  
The Mean ◽  
Mean Field Approximations

Mean-field models are an established method to analyze large stochastic systems with N interacting objects by means of simple deterministic equations that are asymptotically correct when N tends to infinity. For finite N, mean-field equations provide an approximation whose accuracy is model- and parameter-dependent. Recent research has focused on refining the approximation by computing suitable quantities associated with expansions of order $1/N$ and $1/N^2$ to the mean-field equation. In this paper we present a new method for refining mean-field approximations. It couples the master equation governing the evolution of the probability distribution of a truncation of the original state space with a mean-field approximation of a time-inhomogeneous population process that dynamically shifts the truncation across the whole state space. We provide a result of asymptotic correctness in the limit when the truncation covers the state space; for finite truncations, the equations give a correction of the mean-field approximation. We apply our method to examples from the literature to show that, even with modest truncations, it is effective in models that cannot be refined using existing techniques due to non-differentiable drifts, and that it can outperform the state of the art in challenging models that cause instability due orbit cycles in their mean-field equations.

THE DEPENDENCE OF THE NUCLEON MASS ON THE PION MASS IN THE EXTENDED QUARK SIGMA MODEL

2010 ◽  
Vol 19 (10) ◽  
pp. 2051-2062 ◽  
Author(s):  
M. ABU-SHADY
Keyword(s):  
Sigma Model ◽  
Pion Mass ◽  
Mean Field ◽  
Field Approximation ◽  
Linear Sigma Model ◽  
Mean Field Approximation ◽  
Nucleon Mass ◽  
Field Equations ◽  
Analytic Expressions ◽  
The Mean

The dependence of the nucleon mass on the pion mass is studied in the framework of the extended quark sigma model. We apply the modified quark sigma model to analyze the pion–nucleon sigma term. Analytic expressions are derived using the Feynman–Hellman theorem. The field equations are solved in the mean-field approximation. The results are compared with the CP-PACS group and the cloudy bag model. The results indicate that the extended linear sigma model provides good agreement compared to other models in the mean-field approximation.

NUCLEON PROPERTIES FROM MODIFIED SIGMA MODEL

2007 ◽  
Vol 22 (14n15) ◽  
pp. 2673-2681 ◽  
Author(s):  
M. RASHDAN ◽  
M. ABU-SHADY ◽  
T. S. T. ALI
Keyword(s):  
Sigma Model ◽  
Mean Field ◽  
Field Approximation ◽  
Higher Order ◽  
Original Model ◽  
Mean Field Approximation ◽  
Field Equations ◽  
The Mean ◽  
Better Than ◽  
Good Agreement

Birse and Banerjee model is extended to include higher-order mesonic interactions. The field equations have been solved in the mean-field approximation and a good agreement with the data for the nucleon properties has been obtained. The agreement is better than that obtained by the original model of Birse and Banerjee which indicates the important of the inclusion of higher-order meson correlations.

MEAN FIELD APPROXIMATION FOR ANYONS IN A MAGNETIC FIELD

1993 ◽  
Vol 07 (11) ◽  
pp. 2177-2199 ◽  
Author(s):  
ERIK WESTERBERG
Keyword(s):  
Magnetic Field ◽  
Mean Field ◽  
Homogeneous Magnetic Field ◽  
Background Field ◽  
Mean Field Approximation ◽  
Field Energy ◽  
Field Equations ◽  
Statistical Field ◽  
Mean Field Equations ◽  
The Mean

The mean field equations for anyons in an external homogeneous magnetic field are studied and two classes of mean field solutions are explicitly constructed. The first class of solutions, corresponding to one filled Landau level in the combined external and smeared out statistical magnetic fields, is compared to the corresponding exact groundstate solutions. The energies are found to agree well at low densities and when the statistical field is not too strong compared to the background field. At high densities the fermi pressure increases the mean field energy indefinitely. The mean field solutions also miss the lower part of the energy spectrum. To the second class of mean field solutions no corresponding anyon state is known. These solutions represent the anyonic continuation of the fermi state with two filled Landau levels.

QUARK AND SIGMA MASS DEPENDENCE OF NUCLEON PROPERTIES FROM LINEAR SIGMA MODEL

2011 ◽  
Vol 20 (06) ◽  
pp. 1509-1517 ◽  
Author(s):  
T. S. T. ALI
Keyword(s):  
Axial Vector ◽  
Mean Field ◽  
Field Approximation ◽  
Linear Sigma Model ◽  
Mean Field Approximation ◽  
Model Parameters ◽  
Coupling Constant ◽  
Field Equations ◽  
Vector Coupling ◽  
The Mean

The sensitivity of static nucleon properties (magnetic moment, axial-vector coupling constant gA, pion–nucleon coupling constant gπNN and sigma commutator term σπN) to the quark and sigma masses have been investigated in the mean-field approximation. We have solved the field equations in the mean-field approximation with different sets of model parameters. Good results have been obtained in comparison with the other models and experimental data.

EXTENDED LINEAR SIGMA MODEL IN HIGHER ORDER MESONIC INTERACTIONS

2006 ◽  
Vol 15 (01) ◽  
pp. 143-152 ◽  
Author(s):  
M. RASHDAN ◽  
M. ABU-SHADY ◽  
T. S. T. ALI
Keyword(s):  
Sigma Model ◽  
Mean Field ◽  
Field Approximation ◽  
Higher Order ◽  
Linear Sigma Model ◽  
Mean Field Approximation ◽  
Field Equations ◽  
The Mean ◽  
Better Than ◽  
Good Agreement

The Gell-Mann and Levy model, as well as the Birse and Banerjee model, describe quark interactions via the exchange of σ- and π-mesons. We extend these models to include higher order mesonic interactions. The field equations were solved in the mean-field approximation and good agreement with the data for nucleon properties was obtained. Our agreement is better than that obtained by the original model of Birse and Banerjee and by other models. This indicates the importance of including higher order meson correlations.

CHIRAL LOGARITHMIC QUARK MODEL OF N AND Δ WITH AN A-TERM IN THE MEAN-FIELD APPROXIMATION

2011 ◽  
Vol 26 (02) ◽  
pp. 235-249 ◽  
Author(s):  
M. ABU-SHADY
Keyword(s):  
Quark Model ◽  
Sigma Model ◽  
Magnetic Moments ◽  
Mean Field ◽  
Field Approximation ◽  
Mean Field Approximation ◽  
Spontaneous Breaking ◽  
Baryon State ◽  
Field Equations ◽  
The Mean

The A-term is included in the logarithmic quark sigma model, which is based on chiral symmetry and its spontaneous breaking. We investigate the consequences of this term and its relevance to baryon properties. The field equations have been solved in the mean-field approximation for the hedgehog baryon state. We found that including the A-term in the logarithmic quark model leads to lower energies of the nucleon and delta masses and reduces the values of the sigma commutator σπN(0), proton μp(N), and neutron μn(N) of the magnetic moments. This indicates that the inclusion of the A-term improves the calculated nucleon properties in comparison with previous work and other models.

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