Asymptotics of Orthogonal-Polynomial Functionals and Shannon Information Entropy of Rydberg Atoms

2010 ◽  
pp. 93-98
Author(s):  
J. S. Dehesa ◽  
S. López-Rosa ◽  
A. Martínez-Finkelshtein ◽  
R. J. Yáñez
Keyword(s):  
Orthogonal Polynomial ◽  
Information Entropy ◽  
Rydberg Atoms ◽  
Shannon Information ◽  
Shannon Information Entropy ◽  
Polynomial Functionals

scholarly journals Global Seismic Nowcasting With Shannon Information Entropy

2019 ◽  
Vol 6 (1) ◽  
pp. 191-197 ◽  
Author(s):  
John B. Rundle ◽  
Alexis Giguere ◽  
Donald L. Turcotte ◽  
James P. Crutchfield ◽  
Andrea Donnellan
Keyword(s):  
Information Entropy ◽  
Shannon Information ◽  
Shannon Information Entropy

Shannon information entropy sum of the confined hydrogenic atom under the influence of an electric field

2021 ◽  
Vol 75 (4) ◽  
Author(s):  
S. J. C. Salazar ◽  
H. G. Laguna ◽  
B. Dahiya ◽  
V. Prasad ◽  
R. P. Sagar
Keyword(s):  
Electric Field ◽  
Information Entropy ◽  
Shannon Information ◽  
Hydrogenic Atom ◽  
Shannon Information Entropy

scholarly journals Shannon Information Entropy for Seismic Reliability Assessment of Structures Using Self-Organizing Neural Networks

2020 ◽  
Vol 2 (3) ◽  
pp. 1-5
Author(s):  
Mostafa Allameh Zade ◽  
◽  
Iman Amiri ◽  
Keyword(s):  
Neural Network ◽  
Failure Probability ◽  
Information Entropy ◽  
Reliability Assessment ◽  
Computational Effort ◽  
Shannon Information ◽  
Neural Network Models ◽  
Damage Scenarios ◽  
Shannon Information Entropy ◽  
Self Organizing

Localization and quantification of structural damages and find a failure probability is the key important in reliability assessment of structures. In this study, a Self-Organizing Neural Network (SONN) with Shannon Information Entropy simulation is used to reduce the computational effort required for reliability analysis and damage detection. To this end, one demonstrative structure is modeled and then several damage scenarios are defined. These scenarios are considered as training datasets for establishing a Self-Organizing Neural Network model. In this regard, the relation between structural responses (input) and structural stiffness (output) is established using Self-Organizing Neural Network models. The established SONN is more economical and achieves reasonable accuracy in detection of structural damages under ground motion. Furthermore, in order to assess the reliability of structure, five random variables are considered. Namely, columns’ area of first, second and third floor, elasticity modulus and gravity loads. The SONN is trained by Shannon Information Entropy simulation technique. Finally, the trained neural network specifies the failure probability of purposed structure. Although MCS can predict the failure probability for a given structure, the SONN model helps simulation techniques to receive an acceptable accuracy and reduce computational effort.

Calculating the logarithmic mean excitation energy from the Shannon information entropy of the electronic charge density

1998 ◽  
Vol 57 (6) ◽  
pp. 4512-4517 ◽  
Author(s):  
Minhhuy Hồ ◽  
Donald F. Weaver ◽  
Vedene H. Smith ◽  
Robin P. Sagar ◽  
Rodolfo O. Esquivel
Keyword(s):  
Excitation Energy ◽  
Charge Density ◽  
Information Entropy ◽  
Shannon Information ◽  
Electronic Charge ◽  
Logarithmic Mean ◽  
Electronic Charge Density ◽  
Mean Excitation Energy ◽  
Shannon Information Entropy

Shannon-information entropy sum as a correlation measure in atomic systems

2003 ◽  
Vol 67 (1) ◽  
Author(s):  
Nicolais L. Guevara ◽  
Robin P. Sagar ◽  
Rodolfo O. Esquivel
Keyword(s):  
Information Entropy ◽  
Shannon Information ◽  
Correlation Measure ◽  
Atomic Systems ◽  
Shannon Information Entropy
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