scholarly journals Characteristics of Shannon’s Information Entropy of Atomic States in Strongly Coupled Plasma

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 881
Author(s):  
Myoung-Jae Lee ◽  
Young-Dae Jung
Keyword(s):  
Information Entropy ◽  
Entropy Change ◽  
Shannon Information ◽  
Excite State ◽  
Strongly Coupled ◽  
Shannon Information Entropy ◽  
Analytic Expressions ◽  
Strongly Coupled Plasma ◽  
Sphere Radius ◽  
Atomic States

The influence of shielding on the Shannon information entropy for atomic states in strong coupled plasma is investigated using the perturbation method and the Ritz variational method. The analytic expressions for the Shannon information entropies of the ground (1s) and the first excited states (2p) are derived as functions of the ion-sphere radius including the radial and angular parts. It is shown that the entropy change in the atomic state is found to be more significant in the excite state than in the ground state. It is also found that the influence of the localization on the entropy change is more significant for an ion with a higher charge number. The variation of the 1s and 2p Shannon information entropies are discussed.

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

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.

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