arXiv - PHYS - Adaptation and Self-Organizing Systems最新文献

Expected and unexpected routes to synchronization in a system of swarmalators 蜂群系统同步的预期和意外途径

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-09-16 DOI: arxiv-2409.10039

Steve J. Kongni, Thierry Njougouo, Patrick Louodop, Robert Tchitnga, Fernando F. Ferreira, Hilda A. Cerdeira

{"title":"Expected and unexpected routes to synchronization in a system of swarmalators","authors":"Steve J. Kongni, Thierry Njougouo, Patrick Louodop, Robert Tchitnga, Fernando F. Ferreira, Hilda A. Cerdeira","doi":"arxiv-2409.10039","DOIUrl":"https://doi.org/arxiv-2409.10039","url":null,"abstract":"Systems of oscillators whose internal phases and spatial dynamics are\u0000coupled, swarmalators, present diverse collective behaviors which in some cases\u0000lead to explosive synchronization in a finite population as a function of the\u0000coupling parameter between internal phases. Near the synchronization\u0000transition, the phase energy of the particles is represented by the XY model,\u0000and they undergo a transition which can be of the first order or second\u0000depending on the distribution of natural frequencies of their internal\u0000dynamics. The first order transition is obtained after an intermediate state\u0000(Static Wings Phase Wave state (SWPW)) from which the nodes, in cascade over\u0000time, achieve complete phase synchronization at a precise value of the coupling\u0000constant. For a particular case of natural frequencies distribution, a new\u0000phenomenon of Rotational Splintered Phase Wave state (RSpPW) is observed and\u0000leads progressively to synchronization through clusters switching alternatively\u0000from one to two and for which the frequency decreases as the phase coupling\u0000increases.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synchronization cluster bursting in adaptive oscillators networks 自适应振荡器网络中的同步集群突发

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-09-12 DOI: arxiv-2409.08348

Mengke Wei, Andreas Amann, Oleksandr Burylko, Xiujing Han, Serhiy Yanchuk, Jürgen Kurths

引用次数: 0

The forced one-dimensional swarmalator model 强制一维蜂群模型

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-09-09 DOI: arxiv-2409.05342

Md Sayeed Anwar, Dibakar Ghosh, Kevin O'Keeffe

引用次数: 0

Periodic systems have new classes of synchronization stability 周期系统具有新的同步稳定性类别

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-09-06 DOI: arxiv-2409.04193

Sajad Jafari, Atiyeh Bayani, Fatemeh Parastesh, Karthikeyan Rajagopal, Charo I. del Genio, Ludovico Minati, Stefano Boccaletti

{"title":"Periodic systems have new classes of synchronization stability","authors":"Sajad Jafari, Atiyeh Bayani, Fatemeh Parastesh, Karthikeyan Rajagopal, Charo I. del Genio, Ludovico Minati, Stefano Boccaletti","doi":"arxiv-2409.04193","DOIUrl":"https://doi.org/arxiv-2409.04193","url":null,"abstract":"The Master Stability Function is a robust and useful tool for determining the\u0000conditions of synchronization stability in a network of coupled systems. While\u0000a comprehensive classification exists in the case in which the nodes are\u0000chaotic dynamical systems, its application to periodic systems has been less\u0000explored. By studying several well-known periodic systems, we establish a\u0000comprehensive framework to understand and classify their properties of\u0000synchronizability. This allows us to define five distinct classes of\u0000synchronization stability, including some that are unique to periodic systems.\u0000Specifically, in periodic systems, the Master Stability Function vanishes at\u0000the origin, and it can therefore display behavioral classes that are not\u0000achievable in chaotic systems, where it starts, instead, at a strictly positive\u0000value. Moreover, our results challenge the widely-held belief that periodic\u0000systems are easily put in a stable synchronous state, showing, instead, the\u0000common occurrence of a lower threshold for synchronization stability.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reduced-order adaptive synchronization in a chaotic neural network with parameter mismatch: A dynamical system vs. machine learning approach 参数失配混沌神经网络中的低阶自适应同步：动力系统与机器学习方法

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-08-28 DOI: arxiv-2408.16155

Jan Kobiolka, Jens Habermann, Marius E. Yamakou

{"title":"Reduced-order adaptive synchronization in a chaotic neural network with parameter mismatch: A dynamical system vs. machine learning approach","authors":"Jan Kobiolka, Jens Habermann, Marius E. Yamakou","doi":"arxiv-2408.16155","DOIUrl":"https://doi.org/arxiv-2408.16155","url":null,"abstract":"In this paper, we address the reduced-order synchronization problem between\u0000two chaotic memristive Hindmarsh-Rose (HR) neurons of different orders using\u0000two distinct methods. The first method employs the Lyapunov active control\u0000technique. Through this technique, we develop appropriate control functions to\u0000synchronize a 4D chaotic HR neuron (response system) with the canonical\u0000projection of a 5D chaotic HR neuron (drive system). Numerical simulations are\u0000provided to demonstrate the effectiveness of this approach. The second method\u0000is data-driven and leverages a machine learning-based control technique. Our\u0000technique utilizes an ad hoc combination of reservoir computing (RC)\u0000algorithms, incorporating reservoir observer (RO), online control (OC), and\u0000online predictive control (OPC) algorithms. We anticipate our effective\u0000heuristic RC adaptive control algorithm to guide the development of more\u0000formally structured and systematic, data-driven RC control approaches to\u0000chaotic synchronization problems, and to inspire more data-driven neuromorphic\u0000methods for controlling and achieving synchronization in chaotic neural\u0000networks in vivo.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Oscillatory and Excitable Dynamics in an Opinion Model with Group Opinions 有群体意见的舆论模型中的振荡和兴奋动态

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-08-23 DOI: arxiv-2408.13336

Corbit R. Sampson, Mason A. Porter, Juan G. Restrepo

{"title":"Oscillatory and Excitable Dynamics in an Opinion Model with Group Opinions","authors":"Corbit R. Sampson, Mason A. Porter, Juan G. Restrepo","doi":"arxiv-2408.13336","DOIUrl":"https://doi.org/arxiv-2408.13336","url":null,"abstract":"In traditional models of opinion dynamics, each agent in a network has an\u0000opinion and changes in opinions arise from pairwise (i.e., dyadic) interactions\u0000between agents. However, in many situations, groups of individuals can possess\u0000a collective opinion that may differ from the opinions of the individuals. In\u0000this paper, we study the effects of group opinions on opinion dynamics. We\u0000formulate a hypergraph model in which both individual agents and groups of 3\u0000agents have opinions, and we examine how opinions evolve through both dyadic\u0000interactions and group memberships. In some parameter regimes, we find that the\u0000presence of group opinions can lead to oscillatory and excitable opinion\u0000dynamics. In the oscillatory regime, the mean opinion of the agents in a\u0000network has self-sustained oscillations. In the excitable regime, finite-size\u0000effects create large but short-lived opinion swings (as in social fads). We\u0000develop a mean-field approximation of our model and obtain good agreement with\u0000direct numerical simulations. We also show, both numerically and via our\u0000mean-field description, that oscillatory dynamics occur only when the number of\u0000dyadic and polyadic interactions per agent are not completely correlated. Our\u0000results illustrate how polyadic structures, such as groups of agents, can have\u0000important effects on collective opinion dynamics.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boltzmann approach to collective motion via non-local visual interaction 通过非局部视觉互动实现集体运动的波尔兹曼方法

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-08-19 DOI: arxiv-2408.09917

Susumu Ito, Nariya Uchida

{"title":"Boltzmann approach to collective motion via non-local visual interaction","authors":"Susumu Ito, Nariya Uchida","doi":"arxiv-2408.09917","DOIUrl":"https://doi.org/arxiv-2408.09917","url":null,"abstract":"Visual cues play crucial roles in the collective motion of animals, birds,\u0000fish, and insects. The interaction mediated by visual information is\u0000essentially non-local and has many-body nature due to occlusion, which poses a\u0000challenging problem in modeling the emergent collective behavior. In this\u0000Letter, we introduce a Boltzmann-equation approach incorporating non-local\u0000visual interaction. Occlusion is treated in a self-consistent manner via a\u0000coarse-grained density field, which renders the interaction effectively\u0000pairwise. Our model also incorporates the recent finding that each organism\u0000stochastically selects a neighbor to interact at each instant. We analytically\u0000derive the order-disorder transition point, and show that the visual screening\u0000effect substantially raises the transition threshold, which does not vanish\u0000when the density of the agents or the range of the intrinsic interaction is\u0000taken to infinity. Our analysis suggests that the model exhibits a\u0000discontinuous transition as in the local interaction models, and but the\u0000discontinuity is weakened by the non-locality. Our study clarifies the\u0000essential role of non-locality in the visual interactions among moving\u0000organisms.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Why and How do Complex Systems Self-Organize at All? Average Action Efficiency as a Predictor, Measure, Driver, and Mechanism of Self-Organization 复杂系统为何以及如何自我组织？作为自组织的预测、测量、驱动因素和机制的平均行动效率

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-08-17 DOI: arxiv-2408.10278

Matthew J Brouillet, Georgi Yordanov Georgiev

{"title":"Why and How do Complex Systems Self-Organize at All? Average Action Efficiency as a Predictor, Measure, Driver, and Mechanism of Self-Organization","authors":"Matthew J Brouillet, Georgi Yordanov Georgiev","doi":"arxiv-2408.10278","DOIUrl":"https://doi.org/arxiv-2408.10278","url":null,"abstract":"Self-organization in complex systems is a process in which randomness is\u0000reduced and emergent structures appear that allow the system to function in a\u0000more competitive way with other states of the system or with other systems. It\u0000occurs only in the presence of energy gradients, facilitating energy\u0000transmission through the system and entropy production. Being a dynamic\u0000process, self-organization requires a dynamic measure and dynamic principles.\u0000The principles of decreasing unit action and increasing total action are two\u0000dynamic variational principles that are viable to utilize in a self-organizing\u0000system. Based on this, average action efficiency can serve as a quantitative\u0000measure of the degree of self-organization. Positive feedback loops connect\u0000this measure with all other characteristics of a complex system, providing all\u0000of them with a mechanism for exponential growth, and indicating power law\u0000relationships between each of them as confirmed by data and simulations. In\u0000this study, we apply those principles and the model to agent-based simulations.\u0000We find that those principles explain self-organization well and that the\u0000results confirm the model. By measuring action efficiency we can have a new\u0000answer to the question: \"What is complexity and how complex is a system?\". This\u0000work shows the explanatory and predictive power of those models, which can help\u0000understand and design better complex systems.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Networks as Spin Models: From Glass to Hidden Order Through Training 作为旋转模型的神经网络：通过训练从玻璃到隐藏秩序

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-08-12 DOI: arxiv-2408.06421

Richard Barney, Michael Winer, Victor Galitski

{"title":"Neural Networks as Spin Models: From Glass to Hidden Order Through Training","authors":"Richard Barney, Michael Winer, Victor Galitski","doi":"arxiv-2408.06421","DOIUrl":"https://doi.org/arxiv-2408.06421","url":null,"abstract":"We explore a one-to-one correspondence between a neural network (NN) and a\u0000statistical mechanical spin model where neurons are mapped to Ising spins and\u0000weights to spin-spin couplings. The process of training an NN produces a family\u0000of spin Hamiltonians parameterized by training time. We study the magnetic\u0000phases and the melting transition temperature as training progresses. First, we\u0000prove analytically that the common initial state before training--an NN with\u0000independent random weights--maps to a layered version of the classical\u0000Sherrington-Kirkpatrick spin glass exhibiting a replica symmetry breaking. The\u0000spin-glass-to-paramagnet transition temperature is calculated. Further, we use\u0000the Thouless-Anderson-Palmer (TAP) equations--a theoretical technique to\u0000analyze the landscape of energy minima of random systems--to determine the\u0000evolution of the magnetic phases on two types of NNs (one with continuous and\u0000one with binarized activations) trained on the MNIST dataset. The two NN types\u0000give rise to similar results, showing a quick destruction of the spin glass and\u0000the appearance of a phase with a hidden order, whose melting transition\u0000temperature $T_c$ grows as a power law in training time. We also discuss the\u0000properties of the spectrum of the spin system's bond matrix in the context of\u0000rich vs. lazy learning. We suggest that this statistical mechanical view of NNs\u0000provides a useful unifying perspective on the training process, which can be\u0000viewed as selecting and strengthening a symmetry-broken state associated with\u0000the training task.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cybloids $-$ Creation and Control of Cybernetic Colloids 电子胶体 $-$ 电子胶体的创造与控制

arXiv - PHYS - Adaptation and Self-Organizing Systems Pub Date : 2024-08-01 DOI: arxiv-2408.00336

Debasish Saha, Sonja Tarama, Hartmut Löwen, Stefan U. Egelhaaf

{"title":"Cybloids $-$ Creation and Control of Cybernetic Colloids","authors":"Debasish Saha, Sonja Tarama, Hartmut Löwen, Stefan U. Egelhaaf","doi":"arxiv-2408.00336","DOIUrl":"https://doi.org/arxiv-2408.00336","url":null,"abstract":"Colloids play an important role in fundamental science as well as in nature\u0000and technology. They have had a strong impact on the fundamental understanding\u0000of statistical physics. For example, colloids have helped to obtain a better\u0000understanding of collective phenomena, ranging from phase transitions and glass\u0000formation to the swarming of active Brownian particles. Yet the success of\u0000colloidal systems hinges crucially on the specific physical and chemical\u0000properties of the colloidal particles, i.e. particles with the appropriate\u0000characteristics must be available. Here we present an idea to create particles\u0000with freely selectable properties. The properties might depend, for example, on\u0000the presence of other particles (hence mimicking specific pair or many-body\u0000interactions), previous configurations (hence introducing some memory or\u0000feedback), or a directional bias (hence changing the dynamics). Without\u0000directly interfering with the sample, each particle is fully controlled and can\u0000receive external commands through a predefined algorithm that can take into\u0000account any input parameters. This is realized with computer-controlled\u0000colloids, which we term cybloids - short for cybernetic colloids. The potential\u0000of cybloids is illustrated by programming a time-delayed external potential\u0000acting on a single colloid and interaction potentials for many colloids. Both\u0000an attractive harmonic potential and an annular potential are implemented. For\u0000a single particle, this programming can cause subdiffusive behavior or lend\u0000activity. For many colloids, the programmed interaction potential allows to\u0000select a crystal structure at wish. Beyond these examples, we discuss further\u0000opportunities which cybloids offer.","PeriodicalId":501305,"journal":{"name":"arXiv - PHYS - Adaptation and Self-Organizing Systems","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0