Initial-state Dependence of Phase Behaviors in a Dense Active System

Chinese Physics B（2023）SCI 3区

Beijing Normal Univ

Cited 1|Views8

Abstract

There are rich emergent phase behaviors in non-equilibrium active systems.Flocking and clustering are two repre-sentative dynamic phases.The relationship between both the phases is still unclear.Herein,we numerically investigate the evolution of flocking and clustering in a system consisting of self-propelled particles with active reorientation.We consider the interplay between flocking and clustering phases with different initial configurations,and observe a domain in steady state order parameter phase diagrams sensitive to the choice of initial configurations.Specifically,by tuning the initial degree of polar ordering,either a more ordered flocking or a disordered clustering state can be observed in the steady state.These results enlighten us to manipulate emergent behaviors and collective motions of an active system,and are qualitatively different from the emergence of a new bi-stable regime observed in aligned active particles due to an explicit attraction[New J.Phys.14 073033(2012)].

Translated text

Key words

initial state,flocking,clustering,active systems

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper