2022
DOI: 10.1088/1674-1056/ac7cce
|View full text |Cite
|
Sign up to set email alerts
|

Graph dynamical networks for forecasting collective behavior of active matter

Abstract: After decades of theoretical studies, the rich phase states of active matter and cluster kinetic processes are still of research interest. And how to efficiently calculate the dynamical processes under their complex conditions becomes an open problem. Recently, machine learning methods have been proposed to predict the degree of coherence of active matter systems. In this way, the phase transition process of the system is quantified and studied. In this paper, we use Graph Network as a powerful model to determ… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
0
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 45 publications
0
0
0
Order By: Relevance
“…[20][21][22][23][24][25] Since the past decade, machine learning has been used to solve classic problems in complex physical systems. [26][27][28][29][30][31][32][33][34] In particular, the graph network-based simulators show extraordinary predictive capabilities for physical systems such as fluids and rigid solids, thanks to the ability of graph networks to effectively capture relational information and structural properties in the system. [35] At the same time, researchers have successfully used the features of GNNs to predict the longterm dynamics of glass systems.…”
Section: Introductionmentioning
confidence: 99%
“…[20][21][22][23][24][25] Since the past decade, machine learning has been used to solve classic problems in complex physical systems. [26][27][28][29][30][31][32][33][34] In particular, the graph network-based simulators show extraordinary predictive capabilities for physical systems such as fluids and rigid solids, thanks to the ability of graph networks to effectively capture relational information and structural properties in the system. [35] At the same time, researchers have successfully used the features of GNNs to predict the longterm dynamics of glass systems.…”
Section: Introductionmentioning
confidence: 99%