Academic literature on the topic 'Complex dynamical network models'
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Journal articles on the topic "Complex dynamical network models"
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WANG, XIAO FAN. "COMPLEX NETWORKS: TOPOLOGY, DYNAMICS AND SYNCHRONIZATION." International Journal of Bifurcation and Chaos 12, no. 05 (May 2002): 885–916. http://dx.doi.org/10.1142/s0218127402004802.
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Dramatic advances in the field of complex networks have been witnessed in the past few years. This paper reviews some important results in this direction of rapidly evolving research, with emphasis on the relationship between the dynamics and the topology of complex networks. Basic quantities and typical examples of various complex networks are described; and main network models are introduced, including regular, random, small-world and scale-free models. The robustness of connectivity and the epidemic dynamics in complex networks are also evaluated. To that end, synchronization in various dynamical networks are discussed according to their regular, small-world and scale-free connections.
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Wu, Xu, Guo-Ping Jiang, and Xinwei Wang. "A New Model for Complex Dynamical Networks Considering Random Data Loss." Entropy 21, no. 8 (August 15, 2019): 797. http://dx.doi.org/10.3390/e21080797.
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Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming at the data loss which may take place in the communication between any pair of directly connected nodes in a complex dynamical network, we propose a new discrete-time complex dynamical network model by constructing an auxiliary observer and choosing the observer states to compensate for the lost states in the coupling term. By employing Lyapunov stability theory and stochastic analysis, a sufficient condition is derived to guarantee the compensation values finally equal to the lost values, namely, the influence of data loss is finally eliminated in the proposed model. Moreover, we generalize the modeling method to output-coupling complex dynamical networks. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed model.
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Gupta, Abhinav, and Pierre F. J. Lermusiaux. "Neural closure models for dynamical systems." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 477, no. 2252 (August 2021): 20201004. http://dx.doi.org/10.1098/rspa.2020.1004.
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Complex dynamical systems are used for predictions in many domains. Because of computational costs, models are truncated, coarsened or aggregated. As the neglected and unresolved terms become important, the utility of model predictions diminishes. We develop a novel, versatile and rigorous methodology to learn non-Markovian closure parametrizations for known-physics/low-fidelity models using data from high-fidelity simulations. The new neural closure models augment low-fidelity models with neural delay differential equations (nDDEs), motivated by the Mori–Zwanzig formulation and the inherent delays in complex dynamical systems. We demonstrate that neural closures efficiently account for truncated modes in reduced-order-models, capture the effects of subgrid-scale processes in coarse models and augment the simplification of complex biological and physical–biogeochemical models. We find that using non-Markovian over Markovian closures improves long-term prediction accuracy and requires smaller networks. We derive adjoint equations and network architectures needed to efficiently implement the new discrete and distributed nDDEs, for any time-integration schemes and allowing non-uniformly spaced temporal training data. The performance of discrete over distributed delays in closure models is explained using information theory, and we find an optimal amount of past information for a specified architecture. Finally, we analyse computational complexity and explain the limited additional cost due to neural closure models.
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TADIĆ, BOSILJKA, G. J. RODGERS, and STEFAN THURNER. "TRANSPORT ON COMPLEX NETWORKS: FLOW, JAMMING AND OPTIMIZATION." International Journal of Bifurcation and Chaos 17, no. 07 (July 2007): 2363–85. http://dx.doi.org/10.1142/s0218127407018452.
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Many transport processes on networks depend crucially on the underlying network geometry, although the exact relationship between the structure of the network and the properties of transport processes remain elusive. In this paper, we address this question by using numerical models in which both structure and dynamics are controlled systematically. We consider the traffic of information packets that include driving, searching and queuing. We present the results of extensive simulations on two classes of networks; a correlated cyclic scale-free network and an uncorrelated homogeneous weakly clustered network. By measuring different dynamical variables in the free flow regime we show how the global statistical properties of the transport are related to the temporal fluctuations at individual nodes (the traffic noise) and the links (the traffic flow). We then demonstrate that these two network classes appear as representative topologies for optimal traffic flow in the regimes of low density and high density traffic, respectively. We also determine statistical indicators of the pre-jamming regime on different network geometries and discuss the role of queuing and dynamical betweenness for the traffic congestion. The transition to the jammed traffic regime at a critical posting rate on different network topologies is studied as a phase transition with an appropriate order parameter. We also address several open theoretical problems related to the network dynamics.
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O'Dea, Reuben, Jonathan J. Crofts, and Marcus Kaiser. "Spreading dynamics on spatially constrained complex brain networks." Journal of The Royal Society Interface 10, no. 81 (April 6, 2013): 20130016. http://dx.doi.org/10.1098/rsif.2013.0016.
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The study of dynamical systems defined on complex networks provides a natural framework with which to investigate myriad features of neural dynamics and has been widely undertaken. Typically, however, networks employed in theoretical studies bear little relation to the spatial embedding or connectivity of the neural networks that they attempt to replicate. Here, we employ detailed neuroimaging data to define a network whose spatial embedding represents accurately the folded structure of the cortical surface of a rat brain and investigate the propagation of activity over this network under simple spreading and connectivity rules. By comparison with standard network models with the same coarse statistics, we show that the cortical geometry influences profoundly the speed of propagation of activation through the network. Our conclusions are of high relevance to the theoretical modelling of epileptic seizure events and indicate that such studies which omit physiological network structure risk simplifying the dynamics in a potentially significant way.
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House, Thomas, and Matt J. Keeling. "Insights from unifying modern approximations to infections on networks." Journal of The Royal Society Interface 8, no. 54 (June 10, 2010): 67–73. http://dx.doi.org/10.1098/rsif.2010.0179.
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Networks are increasingly central to modern science owing to their ability to conceptualize multiple interacting components of a complex system. As a specific example of this, understanding the implications of contact network structure for the transmission of infectious diseases remains a key issue in epidemiology. Three broad approaches to this problem exist: explicit simulation; derivation of exact results for special networks; and dynamical approximations. This paper focuses on the last of these approaches, and makes two main contributions. Firstly, formal mathematical links are demonstrated between several prima facie unrelated dynamical approximations. And secondly, these links are used to derive two novel dynamical models for network epidemiology, which are compared against explicit stochastic simulation. The success of these new models provides improved understanding about the interaction of network structure and transmission dynamics.
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Uthamacumaran, A. "A Review of Complex Systems Approaches to Cancer Networks." Complex Systems 29, no. 4 (December 15, 2020): 779–835. http://dx.doi.org/10.25088/complexsystems.29.4.779.
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Cancers remain the leading cause of disease-related pediatric death in North America. The emerging field of complex systems has redefined cancer networks as a computational system. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple strange attractors. Machine learning, network science and algorithmic information dynamics are discussed as current tools for cancer network reconstruction. Deep learning architectures and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision-making is investigated within the framework of complex systems and complexity theory.
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HOLME, PETTER. "CONGESTION AND CENTRALITY IN TRAFFIC FLOW ON COMPLEX NETWORKS." Advances in Complex Systems 06, no. 02 (June 2003): 163–76. http://dx.doi.org/10.1142/s0219525903000803.
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The central points of communication network flow have often been identified using graph theoretical centrality measures. In real networks, the state of traffic density arises from an interplay between the dynamics of the flow and the underlying network structure. In this work we investigate the relationship between centrality measures and the density of traffic for some simple particle hopping models on networks with emerging scale-free degree distributions. We also study how the speed of the dynamics are affected by the underlying network structure. Among other conclusions, we find that, even at low traffic densities, the dynamical measure of traffic density (the occupation ratio) has a non-trivial dependence on the static centrality (quantified by "betweenness centrality"), where non-central vertices get a comparatively large portion of the traffic.
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Hanel, Rudolf, Manfred Pöchacker, and Stefan Thurner. "Living on the edge of chaos: minimally nonlinear models of genetic regulatory dynamics." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1933 (December 28, 2010): 5583–96. http://dx.doi.org/10.1098/rsta.2010.0267.
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Linearized catalytic reaction equations (modelling, for example, the dynamics of genetic regulatory networks), under the constraint that expression levels, i.e. molecular concentrations of nucleic material, are positive, exhibit non-trivial dynamical properties, which depend on the average connectivity of the reaction network. In these systems, an inflation of the edge of chaos and multi-stability have been demonstrated to exist. The positivity constraint introduces a nonlinearity, which makes chaotic dynamics possible. Despite the simplicity of such minimally nonlinear systems , their basic properties allow us to understand the fundamental dynamical properties of complex biological reaction networks. We analyse the Lyapunov spectrum, determine the probability of finding stationary oscillating solutions, demonstrate the effect of the nonlinearity on the effective in- and out-degree of the active interaction network , and study how the frequency distributions of oscillatory modes of such a system depend on the average connectivity.
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Ma’ayan, Avi. "Colliding Dynamical Complex Network Models: Biological Attractors versus Attractors from Material Physics." Biophysical Journal 103, no. 9 (November 2012): 1816–17. http://dx.doi.org/10.1016/j.bpj.2012.09.019.
Full textDissertations / Theses on the topic "Complex dynamical network models"
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Spencer, Matthew. "Evolving complex network models of functional connectivity dynamics." Thesis, University of Reading, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590143.
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Functional connectivity networks describe how regions of the brain interact. The timing, location, and frequency of these interactions inform about memory, decision making, motor movement, affective states, and more. However, while these interactions are well described as networks, these networks, like many others throughout nature, are constantly changing. Complex network evolution poses a highly dimensional problem but also contains much information about the system in question. In this thesis, a recent class of evolving complex network models was explored and extended to capture the functional connectivity dynamics observed in neuronal networks. Functional connectivity was investigated through data- and model-driven techniques at the cellular level, with cultures of cortical neurones on multi-electrode arrays, and at the whole-brain level, with electroencephalography. At the neuronal level, complex spatial dependencies were identified in bursts of excitation and two novel network models, the Starburst model and the Excitation Flow model, are used to capture the resulting functional connectivity. At the whole-brain level, functional connectivity dynamics were used to perform single-trial classification of intentional motor movement. Again, spatiotemporal dependencies were identified and used to present three novel techniques for modelling the network dynamics. The first two techniques decomposed networks into network templates (one model-based and one spectral-based) and modelled the dynamics with hidden Markov models. The final technique was a generalised evolving version of the Starburst model. The hidden Markov model of spectrally decomposed networks was shown to classify motor intentions with an accuracy around 80%. Firstly, this thesis shows that time plays an important role in the production of the complex network topologies observed in functional connectivity, both at the cellular and whole-brain leve1. Further, it is shown that evolving complex network models are very useful tools for modelling these topologies and that the network dynamics can be used to uncover features that are crucial to identifying functional states.
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Preciado, Víctor Manuel. "Spectral analysis for stochastic models of large-scale complex dynamical networks." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45873.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 179-196).
Research on large-scale complex networks has important applications in diverse systems of current interest, including the Internet, the World-Wide Web, social, biological, and chemical networks. The growing availability of massive databases, computing facilities, and reliable data analysis tools has provided a powerful framework to explore structural properties of such real-world networks. However, one cannot efficiently retrieve and store the exact or full topology for many large-scale networks. As an alternative, several stochastic network models have been proposed that attempt to capture essential characteristics of such complex topologies. Network researchers then use these stochastic models to generate topologies similar to the complex network of interest and use these topologies to test, for example, the behavior of dynamical processes in the network. In general, the topological properties of a network are not directly evident in the behavior of dynamical processes running on it. On the other hand, the eigenvalue spectra of certain matricial representations of the network topology do relate quite directly to the behavior of many dynamical processes of interest, such as random walks, Markov processes, virus/rumor spreading, or synchronization of oscillators in a network. This thesis studies spectral properties of popular stochastic network models proposed in recent years. In particular, we develop several methods to determine or estimate the spectral moments of these models. We also present a variety of techniques to extract relevant spectral information from a finite sequence of spectral moments. A range of numerical examples throughout the thesis confirms the efficacy of our approach. Our ultimate objective is to use such results to understand and predict the behavior of dynamical processes taking place in large-scale networks.
by Víctor Manuel Preciado.
Ph.D.
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Zschaler, Gerd. "Adaptive-network models of collective dynamics." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-89260.
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Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom.
In this thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system\'s collective and long-term behaviour by applying tools from dynamical systems theory.
We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects\' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge. Moreover, we show what minimal microscopic interaction rules determine whether the transition to collective motion is continuous or discontinuous.
Second, we consider a model of opinion formation in groups of individuals, where we focus on the effect of directed links in adaptive networks. Extending the adaptive voter model to directed networks, we find a novel fragmentation mechanism, by which the network breaks into distinct components of opposing agents. This fragmentation is mediated by the formation of self-stabilizing structures in the network, which do not occur in the undirected case. We find that they are related to degree correlations stemming from the interplay of link directionality and adaptive topological change.
Third, we discuss a model for the evolution of cooperation among self-interested agents, in which the adaptive nature of their interaction network gives rise to a novel dynamical mechanism promoting cooperation. We show that even full cooperation can be achieved asymptotically if the networks\' adaptive response to the agents\' dynamics is sufficiently fast.
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Zschaler, Gerd. "Adaptive-network models of collective dynamics." Doctoral thesis, Max-Planck-Institut für Physik komplexer Systeme, 2011. https://tud.qucosa.de/id/qucosa%3A26056.
Full textAbstract:
Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom.
In this thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system\'s collective and long-term behaviour by applying tools from dynamical systems theory.
We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects\' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge. Moreover, we show what minimal microscopic interaction rules determine whether the transition to collective motion is continuous or discontinuous.
Second, we consider a model of opinion formation in groups of individuals, where we focus on the effect of directed links in adaptive networks. Extending the adaptive voter model to directed networks, we find a novel fragmentation mechanism, by which the network breaks into distinct components of opposing agents. This fragmentation is mediated by the formation of self-stabilizing structures in the network, which do not occur in the undirected case. We find that they are related to degree correlations stemming from the interplay of link directionality and adaptive topological change.
Third, we discuss a model for the evolution of cooperation among self-interested agents, in which the adaptive nature of their interaction network gives rise to a novel dynamical mechanism promoting cooperation. We show that even full cooperation can be achieved asymptotically if the networks\' adaptive response to the agents\' dynamics is sufficiently fast.
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Kolgushev, Oleg. "Influence of Underlying Random Walk Types in Population Models on Resulting Social Network Types and Epidemiological Dynamics." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc955128/.
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Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.
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Peron, Thomas Kauê Dal\'Maso. "Dynamics of Kuramoto oscillators in complex networks." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-21092017-100820/.
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Synchronization of an ensemble of oscillators is an emergent phenomenon present in several complex systems, ranging from biological and physical to social and technological systems. The most successful approach to describe how coherent behavior emerges in these complex systems is given by the paradigmatic Kuramoto model. For decades, this model has been traditionally studied in globally coupled topologies. However, besides being intrinsically dynamical, complex systems exhibit very heterogeneous structure, which can be represented as complex networks. This thesis is dedicated to the investigation of fundamental problems regarding the collective dynamics of Kuramoto oscillators coupled in complex networks. First, we address the effects on network dynamics caused by the presence of triangles, which are structural patterns that permeate real-world networks but are absent in random models. By extending the heterogeneous degree mean-field approach to a class of configuration model that generates random networks with variable clustering, we show that triangles weakly affect the onset of synchronization. Our results suggest that, at least in the low clustering regime, the dynamics of clustered networks are accurately described by tree-based theories. Secondly, we analyze the influence of inertia in the phases evolutions. More precisely, we substantially extend the mean-field calculations to second-order Kuramoto oscillators in uncorrelated networks. Thereby hysteretic transitions of the order parameter are predicted with good agreement with simulations. Effects of degree-degree correlations are also numerically scrutinized. In particular, we find an interesting dynamical equivalence between variations in assortativity and damping coefficients. Potential implications to real-world applications are discussed. Finally, we tackle the problem of two intertwined populations of stochastic oscillators subjected to asymmetric attractive and repulsive couplings. By employing the Gaussian approximation technique we derive a reduced set of ODEs whereby a thorough bifurcation analysis is performed revealing a rich phase diagram. Precisely, besides incoherence and partial synchronization, peculiar states are uncovered in which two clusters of oscillators emerge. If the phase lag between these clusters lies between zero and π, a spontaneous drift different from the natural rhythm of oscillation emerges. Similar dynamical patterns are found in chaotic oscillators under analogous couplings schemes.Sincronização de conjuntos de osciladores é um fenômeno emergente que permeia sistemas complexos de diversas naturezas, como por exemplo, sistemas biológicos, físicos, naturais e tecnológicos. A abordagem mais bem sucedida na descrição da emergência de comportamento coletivo em sistemas complexos é fornecida pelo modelo de Kuramoto. Durante décadas, este modelo foi tradicionalmente estudado em topologias completamente conectadas. Entretanto, além de ser intrinsecamente dinâmicos, tais sistemas complexos possuem uma estrutura altamente heterogênea que pode ser apropriadamente representada por redes complexas. Esta tese é dedicada à investigação de problemas fundamentais da dinâmica coletiva de osciladores de Kuramoto acoplados em redes. Primeiramente, abordamos os efeitos sobre a dinâmica das redes causados pela presença de triângulos padrões que estão omnipresentes em redes reais mas estão ausentes em redes gerados por modelos aleatórios. Estendemos a abordagem via campo-médio para uma variação do modelo de configuração tradicional capaz de criar topologias com número variável de triângulos. Através desta abordagem, mostramos que tais padrões estruturais pouco influenciam a emergência de comportamento coletivo em redes, podendo a dinâmica destas ser descrita em termos de teorias desenvolvidas para redes com topologia local semelhante a grafos de tipo árvore. Em seguida, analisamos a influência de inércia na evolução das fases. Mais precisamente, generalizamos cálculos de campo-médio para osciladores de segunda-ordem acoplados em redes sem correlação de grau. Demonstramos que na presença de efeitos inerciais o parâmetro de ordem do sistema se comporta de forma histerética. Ademais, efeitos oriundos de correlações de grau são examinados. Em particular, verificamos uma interessante equivalência dinâmica entre variações nos coeficientes de assortatividade e amortecimento dos osciladores. Possíveis aplicações para situações reais são discutidas. Finalmente, abordamos o problema de duas populações de osciladores estocásticos sob a influência de acoplamentos atrativos e repulsivos. Através da aplicação da aproximação Gaussiana, derivamos um conjunto reduzido de EDOs através do qual as bifurcações do sistema foram analisadas. Além dos estados asíncrono e síncrono, verificamos a existência de padrões peculiares na dinâmica de tal sistema. Mais precisamente, observamos a formação de estados caracterizados pelo surgimento de dois aglomerados de osciladores. Caso a defasagem entre estes grupos é inferior a π, um novo ritmo de oscilação diferente da frequência natural dos vértices emerge. Comportamentos dinâmicos similares são observados em osciladores caóticos sujeitos a acoplamentos análogos.
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Lenormand, Maxime. "Initialize and Calibrate a Dynamic Stochastic Microsimulation Model: Application to the SimVillages Model." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2012. http://tel.archives-ouvertes.fr/tel-00764929.
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Le but de cette thèse est de développer des outils statistiques permettant d'initialiser et de calibrer les modèles de microsimulation dynamique stochastique, en partant de l'exemple du modèle SimVillages (développé dans le cadre du projet Européen PRIMA). Ce modèle couple des dynamiques démographiques et économiques appliquées à une population de municipalités rurales. Chaque individu de la population, représenté explicitement dans un ménage au sein d'une commune, travaille éventuellement dans une autre, et possède sa propre trajectoire de vie. Ainsi, le modèle inclut-il des dynamiques de choix de vie, d'étude, de carrière, d'union, de naissance, de divorce, de migration et de décès. Nous avons développé, implémenté et testé les modèles et méthodes suivants: * un modèle permettant de générer une population synthétique à partir de données agrégées, où chaque individu est membre d'un ménage, vit dans une commune et possède un statut au regard de l'emploi. Cette population synthétique est l'état initial du modèle. * un modèle permettant de simuler une table d'origine-destination des déplacements domicile-travail à partir de données agrégées. * un modèle permettant d'estimer le nombre d'emplois dans les services de proximité dans une commune donnée en fonction de son nombre d'habitants et de son voisinage en termes de service. * une méthode de calibration des paramètres inconnus du modèle SimVillages de manière à satisfaire un ensemble de critères d'erreurs définis sur des sources de données hétérogènes. Cette méthode est fondée sur un nouvel algorithme d'échantillonnage séquentiel de type Approximate Bayesian Computation.
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Guan, Jinyan. "Bayesian Generative Modeling of Complex Dynamical Systems." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612950.
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This dissertation presents a Bayesian generative modeling approach for complex dynamical systems for emotion-interaction patterns within multivariate data collected in social psychology studies. While dynamical models have been used by social psychologists to study complex psychological and behavior patterns in recent years, most of these studies have been limited by using regression methods to fit the model parameters from noisy observations. These regression methods mostly rely on the estimates of the derivatives from the noisy observation, thus easily result in overfitting and fail to predict future outcomes. A Bayesian generative model solves the problem by integrating the prior knowledge of where the data comes from with the observed data through posterior distributions. It allows the development of theoretical ideas and mathematical models to be independent of the inference concerns. Besides, Bayesian generative statistical modeling allows evaluation of the model based on its predictive power instead of the model residual error reduction in regression methods to prevent overfitting in social psychology data analysis. In the proposed Bayesian generative modeling approach, this dissertation uses the State Space Model (SSM) to model the dynamics of emotion interactions. Specifically, it tests the approach in a class of psychological models aimed at explaining the emotional dynamics of interacting couples in committed relationships. The latent states of the SSM are composed of continuous real numbers that represent the level of the true emotional states of both partners. One can obtain the latent states at all subsequent time points by evolving a differential equation (typically a coupled linear oscillator (CLO)) forward in time with some known initial state at the starting time. The multivariate observed states include self-reported emotional experiences and physiological measurements of both partners during the interactions. To test whether well-being factors, such as body weight, can help to predict emotion-interaction patterns, we construct functions that determine the prior distributions of the CLO parameters of individual couples based on existing emotion theories. Besides, we allow a single latent state to generate multivariate observations and learn the group-shared coefficients that specify the relationship between the latent states and the multivariate observations. Furthermore, we model the nonlinearity of the emotional interaction by allowing smooth changes (drift) in the model parameters. By restricting the stochasticity to the parameter level, the proposed approach models the dynamics in longer periods of social interactions assuming that the interaction dynamics slowly and smoothly vary over time. The proposed approach achieves this by applying Gaussian Process (GP) priors with smooth covariance functions to the CLO parameters. Also, we propose to model the emotion regulation patterns as clusters of the dynamical parameters. To infer the parameters of the proposed Bayesian generative model from noisy experimental data, we develop a Gibbs sampler to learn the parameters of the patterns using a set of training couples. To evaluate the fitted model, we develop a multi-level cross-validation procedure for learning the group-shared parameters and distributions from training data and testing the learned models on held-out testing data. During testing, we use the learned shared model parameters to fit the individual CLO parameters to the first 80% of the time points of the testing data by Monte Carlo sampling and then predict the states of the last 20% of the time points. By evaluating models with cross-validation, one can estimate whether complex models are overfitted to noisy observations and fail to generalize to unseen data. I test our approach on both synthetic data that was generated by the generative model and real data that was collected in multiple social psychology experiments. The proposed approach has the potential to model other complex behavior since the generative model is not restricted to the forms of the underlying dynamics.
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Schmeltzer, Christian. "Dynamical properties of neuronal systems with complex network structure." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17470.
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In welcher Weise hängt die Dynamik eines neuronalen Systems von den Eigenschaften seiner Netzwerkstruktur ab? Diese wichtige Fragestellung der Neurowissenschaft untersuchen wir in dieser Dissertation anhand einer analytischen und numerischen Modellierung der Aktivität großer neuronaler Netzwerke mit komplexer Struktur. Im Fokus steht die Relevanz zweier bestimmter Merkmale für die Dynamik: strukturelle Heterogenität und Gradkorrelationen. Ein zentraler Bestandteil der Dissertation ist die Entwicklung einer Molekularfeldnäherung, mit der die mittlere Aktivität heterogener, gradkorrelierter neuronaler Netzwerke berechnet werden kann, ohne dass einzelne Neuronen explizit simuliert werden müssen. Die Netzwerkstruktur wird von einer reduzierten Matrix erfasst, welche die Verbindungsstärke zwischen den Neuronengruppen beschreibt. Für einige generische Zufallsnetzwerke kann diese Matrix analytisch berechnet werden, was eine effiziente Analyse der Dynamik dieser Systeme erlaubt. Mit der Molekularfeldnäherung und numerischen Simulationen zeigen wir, dass assortative Gradkorrelationen einem neuronalen System ermöglichen, seine Aktivität bei geringer externer Anregung aufrecht zu erhalten und somit besonders sensitiv auf schwache Stimuli zu reagieren.An important question in neuroscience is how the structure and dynamics of a neuronal network relate to each other. We approach this problem by modeling the spiking activity of large-scale neuronal networks that exhibit several complex network properties. Our main focus lies on the relevance of two particular attributes for the dynamics, namely structural heterogeneity and degree correlations. As a central result, we introduce a novel mean-field method that makes it possible to calculate the average activity of heterogeneous, degree-correlated neuronal networks without having to simulate each neuron explicitly. We find that the connectivity structure is sufficiently captured by a reduced matrix that contains only the coupling between the populations. With the mean-field method and numerical simulations we demonstrate that assortative degree correlations enhance the network’s ability to sustain activity for low external excitation, thus making it more sensitive to small input signals.
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Colombini, Giulio. "Synchronisation phenomena in complex neuronal networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23904/.
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The phenomenon of neural synchronisation, a simultaneous and repeated firing of clusters of neurons, underlies many physiological functions and pathological manifestations in the brain of humans and animals, ranging from information encoding to epileptic seizures. Neural synchronisation, as a general phenomenon, can be approached theoretically in the framework of Dynamical Systems on Networks. In the present work, we do so by considering complex networks of FitzHugh-Nagumo model neurons. In the first part we consider the most understood models where each neuron treats its presynaptic neurons all on an equal footing, normalising signals with its in-degree. We study the stability of the synchronous state by devising an algorithm that destabilises it by selecting and removing links from the network, so to obtain a bipartite network. The selection is performed using a perturbative expression, which can be regarded as a specialisation of a previously introduced Spectral Centrality measure. The algorithm is tested on Erdős-Renyi, Watts-Strogatz and Barabási-Albert networks, and its behaviour is assessed from a dynamical and from a structural point of view. In the second part we consider the less studied case in which each neuron divides equally its output among the postsynaptic neurons, so to reproduce schematically the situation where a fixed quantity of neurotransmitter is subdivided between several efferent neurons. In this context a self-consistent approach is formulated and its limitations are explored. In order to extend its application to larger networks, a Mean Field Approximation is presented. The predictivity of the Mean Field Approach is then tested on the different random network models, and the results are discussed in terms of the original network properties.
Books on the topic "Complex dynamical network models"
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1965-, Barthélemy Marc, and Vespignani Alessandro 1965-, eds. Dynamical processes on complex networks. Cambridge: Cambridge University Press, 2009.
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G, Chen. Fundamentals of complex networks: Models, structures, and dynamics. Singapore: John Wiley & Sons Inc., 2015.
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Shoikhet, David, and Mark Elin. Linearization Models for Complex Dynamical Systems. Basel: Birkhäuser Basel, 2010. http://dx.doi.org/10.1007/978-3-0346-0509-0.
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1964-, Beim Graben P., ed. Lectures in supercomputational neuroscience: Dynamics in complex brain networks. Berlin: Springer, 2008.
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Abarbanel, Henry. Predicting the Future: Completing Models of Observed Complex Systems. New York, NY: Springer New York, 2013.
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Kuipers, Benjamin. Self-calibrating models for dynamic monitoring and diagnosis: Final report covering the period 1 February 1992 to 31 March 1995. Austin, Tex: University of Texas at Austin, 1996.
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Mikhailov, A. S. From cells to societies: Models of complex coherent action. Berlin: Springer, 2002.
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Mukherjee, Animesh. Dynamics On and Of Complex Networks, Volume 2: Applications to Time-Varying Dynamical Systems. New York, NY: Springer New York, 2013.
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Wuyi, Yue, Takahashi Yataka, and Takagi Hideaki, eds. Advances in queueing theory and network applications. New York, N.Y: Springer, 2009.
Find full textBook chapters on the topic "Complex dynamical network models"
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Friesz, Terry L., and David Bernstein. "Normative Network Models and Their Solution." In Complex Networks and Dynamic Systems, 207–64. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7594-2_6.
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Benner, Peter, Sara Grundel, and Petar Mlinarić. "Clustering-Based Model Order Reduction for Nonlinear Network Systems." In Model Reduction of Complex Dynamical Systems, 75–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72983-7_4.
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Beyer, Andreas. "Network-Based Models in Molecular Biology." In Dynamics On and Of Complex Networks, 35–56. Boston, MA: Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4751-3_3.
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Varela, Luis M., Giulia Rotundo, Marcel Ausloos, and Jesús Carrete. "Complex Network Analysis in Socioeconomic Models." In Dynamic Modeling and Econometrics in Economics and Finance, 209–45. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12805-4_9.
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Becker, Till, and Darja Wagner-Kampik. "Complex Networks in Manufacturing and Logistics: A Retrospect." In Dynamics in Logistics, 57–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88662-2_3.
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AbstractThe methodology to model systems as graphs or networks already exists for a long time. The availability of information technology and computational power has led to a renaissance of the network modeling approach. Scientists have collected data and started to create huge models of complex networks from various domains. Manufacturing and logistics benefits from this development, because material flow systems are predetermined to be modeled as networks. This chapter revisits selected advances in network modeling and analysis in manufacturing and logistics that have been achieved in the last decade. It presents the basic modeling concept, the transition from static to dynamic and stochastic models, and a collection of examples how network models can be applied to contribute to solving problems in planning and control of logistic systems.
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Ghoshal, Gourab. "Some New Applications of Network Growth Models." In Dynamics On and Of Complex Networks, 217–36. Boston, MA: Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4751-3_13.
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Oliveira, Douglas, and Marco Carvalho. "Empirical Models for Complex Network Dynamics: A Preliminary Study." In Lecture Notes in Computer Science, 637–46. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08783-2_55.
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Gao, Yanhui. "Synchronization Dynamics of Complex Network Models with Impulsive Control." In Information Computing and Applications, 553–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25255-6_70.
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Salii, Yaroslav V. "Benchmarking Optimal Control for Network Dynamic Systems with Plausible Epidemic Models." In Complex Networks & Their Applications X, 194–206. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93413-2_17.
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Korošak, Dean, and Sacha Jon Mooney. "Applications of Complex Network Models to Describe Soil Porous Systems." In Quantifying and Modeling Soil Structure Dynamics, 75–92. Madison, WI, USA: American Society of Agronomy and Soil Science Society of America, 2015. http://dx.doi.org/10.2134/advagricsystmodel3.c4.
Full textConference papers on the topic "Complex dynamical network models"
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Yang, Chun-Lin, and C. Steve Suh. "On the Dynamics of Complex Network." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71994.
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Controlling complex network systems is challenging because network systems are highly coupled by ensembles and behaving with uncertainty. A network is composed by nodes and edges. Edges serve as the connection between nodes to exchange state information and further achieve state consensus. Through edges, the dynamics of individual nodes at the local level intimately affects the network dynamics at the global level. As a following bird can occasionally lose visual contact with the target bird in a flock at any moment, the edge between two nodes in a real world network systems is not necessarily always intact. Contrary to common sense, these real-world networks are usually perfectly stable even when the edges between the nodes are unstable. This suggests that not only nodes are dynamical, edges are dynamical, too. Since the edges between the nodes are changing dynamically, network configuration is also dynamical. Further, edges need be defined and quantified so that the unstable connection behavior can be properly described. The paper explores the concepts of statistical mechanics and statistical entropy to address the particular need. Statistical mechanics describes the behavior of a mechanical system that has uncertain states. Statistical entropy on the other hand defines the distribution of the microstates by probability. Entropy provides a measure of the level of network integrity. With entropy, one can assign desired dynamics to the network to ensure desired network property. This work aims to construct a complex network structure model based on the edge dynamics. Coupled with node self-dynamic and consensus law, a general dynamical network model can be constructed.
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Abdelbari, Hassan, and Kamran Shafi. "Optimising a constrained echo state network using evolutionary algorithms for learning mental models of complex dynamical systems." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727822.
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Gao, Ming, and Li Sheng. "Local and global synchronization criteria for a generalized complex dynamical network model." In 2011 23rd Chinese Control and Decision Conference (CCDC). IEEE, 2011. http://dx.doi.org/10.1109/ccdc.2011.5968287.
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Mahajan, R. L. "Strategies for Building Artificial Neural Network Models." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-1464.
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Abstract An artificial neural network (ANN) is a massively parallel, dynamic system of processing elements, neurons, which are connected in complicated patterns to allow for a variety of interactions among the inputs to produce the desired output. It has the ability to learn directly from example data rather than by following the programmed rules based on a knowledge base. There is virtually no limit to what an ANN can predict or decipher, so long as it has been trained properly through examples which encompass the entire range of desired predictions. This paper provides an overview of such strategies needed to build accurate ANN models. Following a general introduction to artificial neural networks, the paper will describe different techniques to build and train ANN models. Step-by-step procedures will be described to demonstrate the mechanics of building neural network models, with particular emphasis on feedforward neural networks using back-propagation learning algorithm. The network structure and pre-processing of data are two significant aspects of ANN model building. The former has a significant influence on the predictive capability of the network [1]. Several studies have addressed the issue of optimal network structure. Kim and May [2] use statistical experimental design to determine an optimal network for a specific application. Bhat and McAvoy [3] propose a stripping algorithm, starting with a large network and then reducing the network complexity by removing unnecessary weights/nodes. This ‘complex-to-simple’ procedure requires heavy and tedious computation. Villiers and Bernard [4] conclude that although there is no significant difference between the optimal performance of one or two hidden layer networks, single layer networks do better classification on average. Marwah et al. [5] advocate a simple-to-complex methodology in which the training starts with the simplest ANN structure. The complexity of the structure is incrementally stepped-up till an acceptable learning performance is obtained. Preprocessing of data can lead to substantial improvements in the training process. Kown et al. [6] propose a data pre-processing algorithm for a highly skewed data set. Marwah et al. [5] propose two different strategies for dealing with the data. For applications with a significant amount of historical data, smart select methodology is proposed that ensures equal weighted distribution of the data over the range of the input parameters. For applications, where there is scarcity of data or where the experiments are expensive to perform, a statistical design of experiments approach is suggested. In either case, it is shown that dividing the data into training, testing and validation ensures an accurate ANN model that has excellent predictive capabilities. The paper also describes recently developed concepts of physical-neural network models and model transfer techniques. In the former, an ANN model is built on the data generated through the ‘first-principles’ analytical or numerical model of the process under consideration. It is shown that such a model, termed as a physical-neural network model has the accuracy of the first-principles model but yet is orders of magnitude faster to execute. In recognition of the fact that such a model has all the approximations that are generally inherent in physical models for many complex processes, model transfer techniques have been developed [6] that allow economical development of accurate process equipment models. Examples from thermally-based materials processing will be described to illustrate the application of the basic concepts involved.
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Fretheim, Tor, Rahmat Shoureshi, Tyrone Vincent, Duane Torgerson, and John Work. "Machine Diagnostics Using Nonlinear Output Observer and Neural Network Models." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2324.
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Abstract Predictive maintenance is rapidly becoming a familiar concept in industrial fault detection. The ability to detect early warning signals in systems in the form of small changes in dynamic behavior is essential to anticipate failures. In general, accurate system models are an essential part of residual based fault detection. However, in complex nonlinear systems, the development of accurate models can be very difficult, thus usually other approaches are often selected. As an alternative to the nonlinear analytical models, neural networks have shown significant potential in accurately representing nonlinear systems. In this paper we show how a system identified by a neural network, and a nonlinear observer can be used to detect changes in system dynamics. The neural network structure and identification have a significant impact on the observer performance. Different methods for observer design, and appropriate neural network structures for fault detection are discussed. The experimental section shows the observer implemented on a thermo fluid system. Several faults are introduced, and the observer prediction is compared to actual data.
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Zhan, Gao, Zhu Qingbo, and Song Tingxin. "Analysis and Research on Dynamic Models of Complex Manufacturing Network Cascading Failures." In 2014 6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2014. http://dx.doi.org/10.1109/ihmsc.2014.101.
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Shettigar, Nandan, Chun-Lin Yang, and C. Steve Suh. "On the Efficacy of Information Transfer in Complex Networks." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-73710.
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Abstract The adaptability of a complex network determines its ability to maintain stability in a time-dependent environment. These change in macrostate dynamics (time-varying properties in the form of adaptations) are facilitated through a respective change in the microstate configurations of a network. Consequently, these configurations are in terms of the cumulative interactions of the constituents which compose the network ensemble. The nonlinear culmination of these interactions (connections) result in emergent patterns. Therefore, defining the local degree of coupling (strength of connected interactions between constituents) and how these change over time is essential to determine the resultant global time-varying properties of a complex network. Thus, this study proposes the parameters of connectivity (degree of coupling) between constituents in terms of efficacy of information transmission and reception. The underlying logic is that the degree of coupling between two nodes (constituents) can be defined in terms of how much information is transmitted by a donor and how well this is received by the recipient. These parameters control the microstate configurations of a complex network from which macrostate dynamics emerges that govern the adaptability of the network. As global network dynamical properties are nonstationary, the individual (local) constituents and their couplings must also exhibit dynamic, nonstationary behaviors to maintain stability. These local factors result in highly nonlinear behaviors which produce an amalgamation of overall (global) synchronous and asynchronous emergent patterns based on a desired objective and physical system constraints. Furthermore, the intrinsically time-variant nature of the individual constituents and their connections have a particular degree of variance in the time and frequency domains. This characteristic of the degree of coupling controls and allows for change in the magnitude of information transfer between nodes (constituents) in the network. Thus, the adaptability of the degree of coupling and is the foundational basis that allows the global collective properties of a network system to have a high degree of adaptability and robustness to time-varying environments (external disruptions that can compromise system stability). Additionally, emergent behaviors result from the constructive (or destructive) interactions of local dynamics which can increase (or decrease) the influence of individual behaviors amidst the scales of a complex network. This produces a mix of global asynchronous and synchronous organization across spatial and temporal scales that correspond to stable ensemble behaviors. These spatiotemporal scales may exhibit statistical self-similarity. The specific type of emergent scales of behavior is regulated by the degree of coupling between constituents. Therefore, effectively regulating the degree of coupling between constituents is a fundamental basis in regulating a complex network’s capability to adapt to disturbances coming from within as well as without. General parameters defining the degree of coupling are the efficacy of information transmission and the efficacy of information reception. In this study, synaptic plasticity (the modulation of the degree of coupling between neurons) in the human brain is used as an example to enumerate how the parameters controlling the degree of coupling between nodes (the efficacy of information transmission and reception) can be defined, modeled and universally implemented to further a comprehensive understanding of the nonlinear and potential chaotic nature of complex networks in general.
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Duhé, Jean-François, Stéphane Victor, Pierre Melchior, Youssef Abdelmounen, and François Roubertie. "Thermal Modeling Using Two-Port Network Impedance Fractional-Order Approximations." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-69968.
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Abstract Sufficiently accurate thermal modeling is necessary for many applications such as heat dissipation, melting processes, building design or even bio-heat transfers in surgery. Circuit models help modeling heat transfer dynamics: this method is simple and is often used to model thermal phenomena. However, such models well approximates low and high frequency behavior but they are not accurate enough in the middle band of interest, thus lacking of precision in dynamical terms. A more complete and accurate description of conductive heat transfer can be obtained by using a two-port network. The resulting analytical expressions are complex and nonlinear in the frequency ω. This complexity in the frequency domain is difficult to handle when it comes to control applications and more specifically in real-time applications such as surgery. Consequently, an analysis of this thermal two-port network in the frequency domain directly leads to fractional-order systems. A frequency domain analysis of the series and shunt impedances will be presented and different approximations will be explored in order to obtain simple but sufficiently precise linear fractional transfer function models. The series impedances are approximated by using asymptotic and pole-zero approximations and the shunt impedance is approximated by using a capacitance approximation and two fractional model approximations.
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Stankevich, Nataliya V., and Aneta Koseska. "Inhibition Of Oscillations In A Heterogeneous Network Of Hodgkin-Huxley-Type Of Models." In 2018 2nd School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR). IEEE, 2018. http://dx.doi.org/10.1109/dcnair.2018.8589222.
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Matuzas, Vaidas, Juozas Augutis, and Eugenijus Uspuras. "Degradation Assessment in Complex Systems." In 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/icone14-89190.
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Components condition could change dynamically because of various dependencies existing in complex systems since usually not only physical connections exist in the system. Various processes (as degradation or wear out, reliability decrease, failures) affecting one component have influence to condition of other components as well. Current paper is devoted to the analysis and development of mathematical models to the reliability assessment of network systems. Degradation as the main process in the network systems is taken into account. The main attention in present work was paid to the mathematical description of the degradation spreading mechanism in network systems. Developed recursive mathematical model allows assess degradation in network systems during various time moments. Developed methods can be applied for assessment of various risk estimates of hazardous systems such as systems at nuclear power plants.
Reports on the topic "Complex dynamical network models"
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Hovakimyan, Naira, Hunmin Kim, Wenbin Wan, and Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-016.
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Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust adaptive control. By leveraging data shared over a cloud network available to all entities, vehicles proactively adapted to new environments on the proactive level, thus coping with large-scale environmental changes. On the reactive level, control-barrier-function-based robust adaptive control with machine learning improved the performance around nominal models, providing performance and control certificates. The proposed research shaped a robust foundation for autonomous driving on cloud-connected highways of the future.
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Соловйов, В. М., and В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.
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From the standpoint of interdisciplinary self-organization theories and synergetics analyzes current approaches to modeling socio-economic systems. It is shown that the complex network paradigm is the foundation on which to build predictive models of complex systems. We consider two algorithms to transform time series or a set of time series to the network: recurrent and graph visibility. For the received network designed dynamic spectral, topological and multiplex measures of complexity. For example, the daily values the stock indices show that most of the complexity measures behaving in a characteristic way in time periods that characterize the different phases of the behavior and state of the stock market. This fact encouraged to use monitoring and prediction of critical and crisis states in socio-economic systems.
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Rupe, Adam. Learning Implicit Models of Complex Dynamical Systems From Partial Observations. Office of Scientific and Technical Information (OSTI), July 2021. http://dx.doi.org/10.2172/1808822.
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Soloviev, Vladimir, Natalia Moiseienko, and Olena Tarasova. Modeling of cognitive process using complexity theory methods. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3609.
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The features of modeling of the cognitive component of social and humanitarian systems have been considered. An example of using multiscale, multifractal and network complexity measures has shown that these and other synergetic models and methods allow us to correctly describe the quantitative differences of cognitive systems. The cognitive process is proposed to be regarded as a separate implementation of an individual cognitive trajectory, which can be represented as a time series and to investigate its static and dynamic features by the methods of complexity theory. Prognostic possibilities of the complex systems theory will allow to correct the corresponding pedagogical technologies.
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Rabitz, Herschel. Closed Loop Adaptive Refinement of Dynamical Models for Complex Chemical Reactions. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada499595.
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Соловйов, Володимир Миколайович, Наталя Володимирівна Моісеєнко, and Олена Юріївна Тарасова. Complexity theory and dynamic characteristics of cognitive processes. Springer, January 2020. http://dx.doi.org/10.31812/123456789/4143.
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The features of modeling of the cognitive component of social and humanitarian systems have been considered. An example of using entropy multiscale, multifractal, recurrence and network complexity measures has shown that these and other synergetic models and methods allow us to correctly describe the quantitative differences of cognitive systems. The cognitive process is proposed to be regarded as a separate implementation of an individual cognitive trajectory, which can be represented as a time series and to investigate its static and dynamic features by the methods of complexity theory. Prognostic possibilities of the complex systems theory will allow to correct the corresponding pedagogical technologies. It has been proposed to track and quantitatively describe the cognitive trajectory using specially transformed computer games which can be used to test the processual characteristics of thinking.
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Водолєєва, І. С., А. О. Лазаренко, and В. М. Соловйов. Дослідження стійкості мультиплексних мереж під час кризових явищ. Видавець Вовчок О.Ю., 2017. http://dx.doi.org/10.31812/0564/1259.
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Demonstrated features of modeling random and directed attacks on the network as the basis for timely monitoring adverse events and to ensure the stability and reliability of the system. A testing system developed indicators robustness for example the actual functioning of complex systems, including a series of attacks on the social, technical and terror networks modeled changing dynamics of the occurrence of such attacks. Analysis of the results gives rise to recommendations for practical application range of indicators developed as a system of sustainable development of complex socio-economic systems.
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Nechaev, V., Володимир Миколайович Соловйов, and A. Nagibas. Complex economic systems structural organization modelling. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1118.
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One of the well-known results of the theory of management is the fact, that multi-stage hierarchical organization of management is unstable. Hence, the ideas expressed in a number of works by Don Tapscott on advantages of network organization of businesses over vertically integrated ones is clear. While studying the basic tendencies of business organization in the conditions of globalization, computerization and internetization of the society and the results of the financial activities of the well-known companies, the authors arrive at the conclusion, that such companies, as IBM, Boeing, Mercedes-Benz and some others companies have not been engaged in their traditional business for a long time. Their partner networks performs this function instead of them. The companies themselves perform the function of system integrators. The Tapscott’s idea finds its confirmation within the framework of a new powerful direction of the development of the modern interdisciplinary science – the theory of the complex networks (CN) [2]. CN-s are multifractal objects, the loss of multifractality being the indicator of the system transition from more complex state into more simple state. We tested the multifractal properties of the data using the wavelet transform modulus maxima approach in order to analyze scaling properties of our company. Comparative analysis of the singularity spectrumf(®), namely, the difference between maximum and minimum values of ® (∆ = ®max ¡ ®min) shows that IBM company is considerably more fractal in comparison with Apple Computer. Really, for it the value of ∆ is equal to 0.3, while for the vertically integrated company Apple it only makes 0.06 – 5 times less. The comparison of other companies shows that this dependence is of general character. Taking into consideration the fact that network organization of business has become dominant in the last 5-10 years, we carried out research for the selected companies in the earliest possible period of time which was determined by the availability of data in the Internet, or by historically later beginning of stock trade of computer companies. A singularity spectrum of the first group of companies turned out to be considerably narrower, or shifted toward the smaller values of ® in the pre-network period. The latter means that dynamic series were antipersistant. That is, these companies‘ management was rigidly controlled while the impact of market mechanisms was minimized. In the second group of companies if even the situation did changed it did not change for the better. In addition, we discuss applications to the construction of portfolios of stock that have a stable ratio of risk to return.
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Thai, My. Combating Weapons of Mass Destruction: Models, Complexity, and Algorithms in Complex Dynamic and Evolving Networks. Fort Belvoir, VA: Defense Technical Information Center, November 2015. http://dx.doi.org/10.21236/ada625120.
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Soloviev, Volodymyr Mykolayovych, and Viktoriya Volodymyrivna Solovyova. Universal tools of modeling different nature complex systems. ФОП Однорог Т.В., 2018. http://dx.doi.org/10.31812/123456789/2865.
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It is shown that there is а powerful set of tools for the study of self-organization in complex systems, both natural and artificial origin. They characterize the multidimensional nature of complexity - multifractality, irreversibility, non-linearity, recurrence, nonstability, emeregence, etc., and quantitative evaluation of individual dynamical measures of complexity allows for monitoring, predicting and preventing unwanted critical or crisis. Particular attention is paid to measures of network complexity, which are fully applicable to build synergistic network of pedagogical systems.