Collective Motion in Finite Many-Particle Systems. II

1965 ◽  
Vol 138 (5B) ◽  
pp. B1323-B1332 ◽  
Author(s):  
Abraham Klein ◽  
Arthur K. Kerman
Keyword(s):  
Collective Motion ◽  
Particle Systems

scholarly journals A WELL-POSEDNESS THEORY IN MEASURES FOR SOME KINETIC MODELS OF COLLECTIVE MOTION

2011 ◽  
Vol 21 (03) ◽  
pp. 515-539 ◽  
Author(s):  
J. A. CAÑIZO ◽  
J. A. CARRILLO ◽  
J. ROSADO
Keyword(s):  
Collective Behavior ◽  
Hydrodynamic Limit ◽  
Collective Motion ◽  
Kinetic Equations ◽  
Particle Systems ◽  
Well Posedness ◽  
Mass Transportation ◽  
Short Range Repulsion ◽  
Large Groups ◽  
Continuous Dependence Results

We present existence, uniqueness and continuous dependence results for some kinetic equations motivated by models for the collective behavior of large groups of individuals. Models of this kind have been recently proposed to study the behavior of large groups of animals, such as flocks of birds, swarms, or schools of fish. Our aim is to give a well-posedness theory for general models which possibly include a variety of effects: an interaction through a potential, such as a short-range repulsion and long-range attraction; a velocity-averaging effect where individuals try to adapt their own velocity to that of other individuals in their surroundings; and self-propulsion effects, which take into account effects on one individual that are independent of the others. We develop our theory in a space of measures, using mass transportation distances. As consequences of our theory, we show also the convergence of particle systems to their corresponding kinetic equations, and the local-in-time convergence to the hydrodynamic limit for one of the models.

Collective motion in many-particle systems

1961 ◽  
Vol 12 (3) ◽  
pp. 452-462 ◽  
Author(s):  
Harry J Lipkin
Keyword(s):  
Collective Motion ◽  
Particle Systems

Collective motion in many-particle systems

1960 ◽  
Vol 9 (2) ◽  
pp. 272-291 ◽  
Author(s):  
Harry J Lipkin
Keyword(s):  
Collective Motion ◽  
Particle Systems

Local interaction rules and collective motion in black neon tetra (Hyphessobrycon herbertaxelrodi) and zebrafish (Danio rerio).

2019 ◽  
Vol 133 (2) ◽  
pp. 143-155 ◽  
Author(s):  
Vicenç Quera ◽  
Elisabet Gimeno ◽  
Francesc S. Beltran ◽  
Ruth Dolado
Keyword(s):  
Danio Rerio ◽  
Collective Motion ◽  
Local Interaction

RELAXATION AND COLLECTIVE MOTION IN SUPERCONDUCTORS. A TWO-FLUID DESCRIPTION

1978 ◽  
Vol 39 (C6) ◽  
pp. C6-488-C6-489 ◽  
Author(s):  
C. J. Pethick ◽  
H. Smith
Keyword(s):  
Collective Motion ◽  
Two Fluid

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

Statistical 3D Analysis and Modeling of Complex Particle Systems based on Tomographic Image Data

2019 ◽  
Vol 56 (12) ◽  
pp. 787-796
Author(s):  
O. Furat ◽  
B. Prifling ◽  
D. Westhoff ◽  
M. Weber ◽  
V. Schmidt
Keyword(s):  
Image Data ◽  
Particle Systems ◽  
3D Analysis ◽  
Tomographic Image ◽  
Complex Particle
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