A comparative study between two models of active cluster crystals

Caprini, Lorenzo; Hernández-Garcı́a, Emilio; López, Cristóbal; Marconi, U.

doi:10.1038/s41598-019-52420-1

Cited by 41 publications

(33 citation statements)

References 98 publications

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“…The AOUP has been employed to reproduce the phenomenology of passive colloids immersed in a bath of active particles [47][48][49][50] but also-perhaps at a more approximate level-the dynamics of self-propelled particles themselves. Its connection with other popular models for self-propelled particles has been addressed by some authors [51,52]. For instance, the AOUP model can reproduce the accumulation near the boundaries of channels and obstacles [41,53], the non-equilibrium clustering or phase-separation [25,54,55] typical of active matter and the spatial velocity correlation spontaneously observed in dense active systems [56,57].…”

Section: Self-propelled Particlesmentioning

confidence: 99%

Fluctuation–Dissipation Relations in Active Matter Systems

2021

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We investigate the non-equilibrium character of self-propelled particles through the study of the linear response of the active Ornstein–Uhlenbeck particle (AOUP) model. We express the linear response in terms of correlations computed in the absence of perturbations, proposing a particularly compact and readable fluctuation–dissipation relation (FDR): such an expression explicitly separates equilibrium and non-equilibrium contributions due to self-propulsion. As a case study, we consider non-interacting AOUP confined in single-well and double-well potentials. In the former case, we also unveil the effect of dimensionality, studying one-, two-, and three-dimensional dynamics. We show that information about the distance from equilibrium can be deduced from the FDR, putting in evidence the roles of position and velocity variables in the non-equilibrium relaxation.

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Section: Self-propelled Particlesmentioning

confidence: 99%

Fluctuation–Dissipation Relations in Active Matter Systems

2021

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“…We study dense systems of N interacting self-propelled particles at density ρ 0 , employing the active Ornstein-Uhlenbeck particle (AOUP) model [50][51][52][53][54][55][56][57]. The AOUP is a versatile and popular model of active matter that can reproduce many aspects of the phenomenology of self-propelled particles, including the accumulation near rigid boundaries [58][59][60][61][62] or obstacles and the motility induced phase separation (MIPS) [63].…”

Section: Modelmentioning

confidence: 99%

Time-dependent properties of interacting active matter: Dynamical behavior of one-dimensional systems of self-propelled particles

Caprini

Marconi

2020

Phys. Rev. Research

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We study an interacting high-density one-dimensional system of self-propelled particles described by the active Ornstein-Uhlenbeck particle model where, even in the absence of alignment interactions, velocity and energy domains spontaneously form in analogy with those already observed in two dimensions. Such domains are regions where the individual velocities are spatially correlated as a result of the interplay between self-propulsion and interactions. Their typical size is controlled by a characteristic correlation length. In this work, we focus on a lesser-known aspect of the model, namely, its dynamical behavior. To this purpose, we investigate theoretically and numerically the time-dependent velocity autocorrelation and spatiotemporal velocity correlation functions. The study of these correlations provides a measure of the average lifetime and, thus, the stability in time of the velocity domains.

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“…First, we show the spontaneous correspondence between the complex system and the structure of AgentNet by providing formulations of both systems. The capability of AgentNet is thor-oughly demonstrated here via data from simulated complex systems: cellular automata 33 , the Vicsek model 1 , and the active Ornstein-Uhlenbeck particle (AOUP) 34 model, along with application to real-world data comprising trajectories in a flock of birds 35 containing more than 1800 agents in a single instance, greatly exceeding the previous range of neural network approaches 18,36,37 which treated at most several dozens of agents. For the simulated systems, we show that each component of AgentNet learns predictable and tractable parts of the expected transition function by comparing extracted features with ground-truth functions.…”

Section: Mainmentioning

confidence: 99%

Unraveling Hidden Interactions in Complex Systems with Deep Learning

Jeong

2020

Preprint

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Rich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by human scientists. In this study, we propose AgentNet, a model-free data-driven framework consisting of deep neural networks to reveal and analyze the hidden interactions in complex systems from observed data alone. AgentNet utilizes a graph attention network with novel variable-wise attention to model the interaction between individual agents, and employs various encoders and decoders that can be selectively applied to any desired system. Our model successfully captured a wide variety of simulated complex systems, namely cellular automata (discrete), the Vicsek model (continuous), and active Ornstein--Uhlenbeck particles (non-Markovian) in which, notably, AgentNet's visualized attention values coincided with the true variable-wise interaction strengths and exhibited collective behavior that was absent in the training data. A demonstration with empirical data from a flock of birds showed that AgentNet could identify hidden interaction ranges exhibited by real birds, which cannot be detected by conventional velocity correlation analysis. We expect our framework to open a novel path to investigating complex systems and to provide insight into general process-driven modeling.

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A comparative study between two models of active cluster crystals

Cited by 41 publications

References 98 publications

Fluctuation–Dissipation Relations in Active Matter Systems

Fluctuation–Dissipation Relations in Active Matter Systems

Time-dependent properties of interacting active matter: Dynamical behavior of one-dimensional systems of self-propelled particles

Unraveling Hidden Interactions in Complex Systems with Deep Learning

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