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The theory of thermodynamic phase transitions has played a central role both in theoretical physics and in dynamical systems for several decades. One of its fundamental results is the classification of various physical models into equivalence classes with respect to the scaling behavior of solutions near the critical manifold. From that point of view, systems characterized by the same set of critical exponents are equivalent, regardless of how different the original physical models might be. For non-equilibrium phase transitions, the current theoretical framework is much less developed. In particular, an equivalent classification criterion is not available, thus requiring a specific analysis of each model individually. In this thesis, we propose a potential classification method for time-dependent dynamical systems, namely comparing the possible deformations of the original problem, and identifying dynamical systems which share the same deformation space. The specific model on which this procedure is developed is the Kuramoto model for interacting, disordered oscillators. Studied in the mean-field limit by a variety of methods, its associated synchronization phase transition appears as an appropriate model for cooperative phenomena ranging from coupled Josephson junctions to self-ordering patterns in biological and social systems. We investigate the geometric deformation of the dynamical system into the space of univalent maps of the unit disk, related to the Douady-Earle extension and the Denjoy-Wolff theory, and separately the algebraic deformation into the space of nonlinear sigma models for unitary operators. The results indicate that the Kuramoto model is representative for a large class of non-equilibrium synchronization models, with a rich phase-space diagram.

Orientadores: Alberto Vazquez Saa, Marcus Aloizio Martinez de Aguiar / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin / Made available in DSpace on 2018-08-28T03:40:49Z (GMT). No. of bitstreams: 1 Pinto_RafaelSoares_D.pdf: 7979471 bytes, checksum: b344e1e01031709b8b938dbecb572900 (MD5) Previous issue date: 2015 / Resumo: Sincronização está presente em uma miríade de situações, indo desde vaga-lumes piscando em uníssono na copa das árvores, populações de leveduras ajustando seu metabolismo para um ritmo comum, atividades neurais ocorrendo no cérebro, chegando até as redes de distribuição de energia elétrica, as maiores máquinas construídas pelo homem. Neste trabalho, nós analisamos como se dá o processo de sincronização utilizando o bem conhecido modelo de Kuramoto, estudado incansavelmente nas últimas décadas, quando ele se encontra sobre uma rede complexa, que determina os padrões de interação entre os elementos que compõem a população. A topologia dessas interações determina de maneira crucial a dinâmica do sistema, possibilitando, ou não, a sincronização dos seus elementos. Primeiros, nós analisamos o fenômeno da sincronização explosiva: a correlação de propriedades da rede com a frequência natural dos osciladores altera dramaticamente a natureza da transição de fase do estado não sincronizado para o estado sincronizado. Mostramos que sincronização explosiva ocorre mesmo quando apenas uma pequena fração dos vértices da rede possuem tal correlação, a saber, os vértices mais bem conectados da rede. Além do mais, ajustando o número de vértices onde a correlação é válida, podemos controlar propriedades dessa transição de fase. A seguir estudamos o processo de optimização de topologia para favorecer sincronização. Dado um conjunto de vértices/osciladores com frequências naturais conhecidas e um certo número de links, qual é a melhor topologia, ou seja, o padrão de conexões, que favorece a sincronização? Estudamos esse problema numericamente para o modelo de Kuramoto com inércia, que serve como um modelo simples para analisar as redes de transmissão de energia elétrica, obtendo princípios básicos que devem ser utilizados para o design de tais sistemas. Por fim, ainda no problema de optimização de topologia para favorecer sincronização, obtivemos pela primeira vez de forma analítica as condições para optimização para o modelo de Kuramoto, bem como para uma generalização sua, onde há interações positivas e negativas. Esses resultados analíticos ainda servem para criar algoritmos de optimização mais ecientes que os utilizados atualmente / Abstract: Synchronization is present in a myriad of situations, from the unison ashing of reies in trees, populations of yeast adjusting their metabolism to a common rhythm, neural activities in the brain to the largest machines ever built, the power grids. We analysed how the process of synchronization happens using the well known Kuramoto model, tirelessly studied in the last decades, when it is on top of a complex network, that determines the patterns of interaction between the elements of the population. The topology of this network's determines crucially the possible dynamics of the systems, allowing, or not, the synchronization of its elements. We rst discuss the phenomenon of explosive synchronization, where the correlation between properties of the network and the oscillators changes drastically the nature of the phase transition separating the incoherent state from the synchronized state.We show that explosive synchronization can occur even when a small subset of the vertices are correlated. It is necessary that only the hubs, vertices with highest degrees, show the correlation. Moreover, adjust the fraction of correlated vertices allows us to control properties of the phase transition. Next we study the optimization of the topology to favor synchronization. Given a set of vertices/oscillators with know natural frequencies and a certain number of links, which is the best topology, its pattern of interactions, to favor synchronization? We studied this problem to a generalized Kuramoto model (Kuramoto model with inertia) that is used as a simple tool to model power grids, obtaining in this way simple rules that can be applied to the design of such systems that already helps the synchronization of its elements. In our nal contribution, still in the optimization of the topology problem, we were able, for the first time, to obtain analytically the conditions of optimization for the Kuramoto model, as well as for one of its generalizations, where there can exist positive and negative interactions between the elements. Beyond the signicant fact that the conditions can be know analytically, these results can be used to obtain faster optimization algorithms that the current ones / Doutorado / Física / Doutor em Ciências / 2012/09357-9 / CAPES

Thesis advisor: Jan R. Engelbrecht / Thesis advisor: Renato E. Mirollo / Many phenomena in nature that involve ordering in time can be understood as collective behavior of coupled oscillators. One paradigm for studying a population of self-sustained oscillators is the Kuramoto model, where each oscillator is described by a phase variable, and interacts with other oscillators through trigonometric functions of phase differences. This dissertation studies $N$ identical Kuramoto oscillators in a general form \[ \dot{\theta}_{j}=A+B\cos\theta_{j}+C\sin\theta_{j}\qquad j=1,\dots,N, \] where coefficients $A$, $B$, and $C$ are symmetric functions of all oscillators $(\theta_{1},\dots,\theta_{N})$. Dynamics of this model live in group orbits of M\"obius transformations, which are low-dimensional manifolds in the full state space. When the system is a phase model (invariant under a global phase shift), trajectories in a group orbit can be identified as flows in the unit disk with an intrinsic hyperbolic metric. A simple criterion for such system to be a gradient flow is found, which leads to new classes of models that can be described by potential or Hamiltonian functions while exhibiting a large number of constants of motions. A generalization to extended phase models with non-identical couplings gives rise to richer structures of fixed points and bifurcations. When the coupling weights sum to zero, the system is simultaneously gradient and Hamiltonian. The flows mimic field lines of a two-dimensional electrostatic system consisting of equal amounts of positive and negative charges. Bifurcations on a partially synchronized subspace are discussed as well. / Thesis (PhD) — Boston College, 2017. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Physics.

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.

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.

This work is concerned with the spatio-temporal structures that emerge when non-identical, diffusively coupled oscillators synchronize. It contains analytical results and their confirmation through extensive computer simulations. We use the Kuramoto model which reduces general oscillatory systems to phase dynamics. The symmetry of the coupling plays an important role for the formation of patterns. We have studied the ordering influence of an asymmetry (non-isochronicity) in the phase coupling function on the phase profile in synchronization and the intricate interplay between this asymmetry and the frequency heterogeneity in the system. The thesis is divided into three main parts. Chapter 2 and 3 introduce the basic model of Kuramoto and conditions for stable synchronization. In Chapter 4 we characterize the phase profiles in synchronization for various special cases and in an exponential approximation of the phase coupling function, which allows for an analytical treatment. Finally, in the third part (Chapter 5) we study the influence of non-isochronicity on the synchronization frequency in continuous, reaction diffusion systems and discrete networks of oscillators. / Die vorliegende Arbeit beschäftigt sich in Theorie und Simulation mit den raum-zeitlichen Strukturen, die entstehen, wenn nicht-identische, diffusiv gekoppelte Oszillatoren synchronisieren. Wir greifen dabei auf die von Kuramoto hergeleiteten Phasengleichungen zurück. Eine entscheidene Rolle für die Musterbildung spielt die Symmetrie der Kopplung. Wir untersuchen den ordnenden Einfluss von Asymmetrie (Nichtisochronizität) in der Phasenkopplungsfunktion auf das Phasenprofil in Synchronisation und das Zusammenspiel zwischen dieser Asymmetrie und der Frequenzheterogenität im System. Die Arbeit gliedert sich in drei Hauptteile. Kapitel 2 und 3 beschäftigen sich mit den grundlegenden Gleichungen und den Bedingungen für stabile Synchronisation. Im Kapitel 4 charakterisieren wir die Phasenprofile in Synchronisation für verschiedene Spezialfälle sowie in der von uns eingeführten exponentiellen Approximation der Phasenkopplungsfunktion. Schliesslich untersuchen wir im dritten Teil (Kap.5) den Einfluss von Nichtisochronizität auf die Synchronisationsfrequenz in kontinuierlichen, oszillatorischen Reaktions-Diffusionssystemen und diskreten Netzwerken von Oszillatoren.

Cette thèse porte sur l'analyse de la synchronisation des grands réseaux d'oscillateurs non linéaires et hétérogènes à l'aide d'outils et de méthodes issues de la théorie du contrôle. Nous considérons deux modèles de réseaux; à savoir, le modèle de Kuramoto qui considère seulement les coordonnées de phase des oscillateurs et des réseaux composés d'oscillateurs non linéaires de Stuart-Landau connectés par un couplage linéaire.Pour le modèle de Kuramoto nous construisons un système linéaire qui conserve les informations sur les fréquences naturelles et sur les gains d'interconnexion du modèle original de Kuramoto. Nous montrons en suite que l'existence de solutions à verrouillage de phase du modèle de Kuramoto est équivalente à l'existence d'un tel système linéaire avec certaines propriétés. Ce système est utilisé pour formuler les conditions d'existence de solutions à verrouillage de phase et de leur stabilité pour des structures particulières de l'interconnexion. Ensuite, cette analyse s'est étendue au cas où des interactions attractives et répulsives sont présentes dans le réseau. Nous considérons cette situation lorsque les gains d'interconnexion peuvent être à la fois positif et négatif. Dans le cadre de réseaux d'oscillateurs de Stuart-Landau, nous présentons une nouvelle transformation de coordonnées du réseau qui permet de réécrire le modèle du réseau en deux parties: une décrivant le comportement de l'oscillateur « moyenne » du réseau et la seconde partie présentant les dynamiques des erreurs de synchronisation par rapport à cet oscillateur « moyenne ». Cette transformation nous permet de caractériser les propriétés du réseau en termes de la stabilité des erreurs de synchronisation et du cycle limite de l'oscillateur « moyenne ». Pour ce faire, nous reformulons ce problème en un problème de stabilité de deux ensembles compacts et nous utilisons des outils issus de la stabilité de Lyapunov pour montrer la stabilité pratique de ces derniers pour des valeurs suffisamment grandes du gain d'interconnexion. / This thesis is devoted to the analysis of synchronization in large networks of heterogeneous nonlinear oscillators using tools and methods issued from control theory. We consider two models of networks; namely, the Kuramoto model which takes into account only phase coordinates of the oscillators and networks composed of nonlinear Stuart-Landau oscillators interconnected by linear coupling. For the Kuramoto model we construct an auxiliary linear system that preserves information on the natural frequencies and interconnection gains of the original Kuramoto model. We show next that existence of phase locked solutions of the Kuramoto model is equivalent to the existence of such a linear system with certain properties. This system is used to formulate conditions that ensure existence of phase-locked solutions and their stability for particular structures of network interconnections. Next, this analysis is extended to the case where both attractive and repulsive interactions are present in the network that is we consider the situation where some of the interconnection gains are allowed to be negative. In the context of networks of Stuart-Landau oscillators, we present a new coordinate transformation of the network which allows to split the network model into two parts, one describing behaviour of an "averaged" network oscillator and the second one, describing dynamics of the synchronization errors relative to this "averaged" oscillator. This transformation allows us to characterize properties of the network in terms of stability of synchronization errors and limit cycle of the "averaged" oscillator. To do so, we recast this problem as a problem of stability of compact sets and use Lyapunov stability tools to ensure practical stability of both sets for sufficiently large values of the coupling strength.

Synchonisation ist ein universelles Phänomen welches in den Natur- und Ingenieurwissenschaften, aber auch in Sozialsystemen vorkommt. Verschiedene Modellsysteme wurden zur Beschreibung von Synchronisation vorgeschlagen, wobei das Kuramoto-Modell das am weitesten verbreitete ist. Das Kuramoto-Modell zweiter Ordnung beschreibt eigenständige Phasenoszillatoren mit heterogenen Eigenfrequenzen, die durch den Sinus ihrer Phasendifferenzen gekoppelt sind, und wird benutzt um nichtlineare Dynamiken in Stromnetzen, Josephson-Kontakten und vielen anderen Systemen zu analysieren. Im Laufe der letzten Jahre wurden insbesondere Netzwerke von Kuramoto-Oszillatoren studiert, da sie einfach genug für eine analytische Beschreibung und denoch reich an vielfältigen Phänomenen sind. Eines dieser Phänomene, explosive synchronization, entsteht in skalenfreien Netzwerken wenn eine Korrelation zwischen den Eigenfrequenzen der Oszillatoren und der Netzwerktopolgie besteht. Im ersten Teil dieser Dissertation wird ein Kuramoto-Netzwerk zweiter Ordnung mit einer Korrelation zwischen den Eigenfrequenzen der Oszillatoren und dem Netzwerkgrad untersucht. Die Theorie im Kontinuumslimit und für unkorrelierte Netzwerke wird für das Modell mit asymmetrischer Eigenfrequenzverteilung entwickelt. Dabei zeigt sich, dass Cluster von Knoten mit demselben Grad nacheinander synchronisieren, beginnend mit dem kleinsten Grad. Dieses neue Phänomen wird als cluster explosive synchronization bezeichnet. Numerische Untersuchungen zeigen, dass dieses Phänomen auch durch die Zusammensetzung der Netzwerkgrade beeinflusst wird. Zum Beispiel entstehen unstetige Übergänge nicht nur in disassortativen, sondern auch in stark assortativen Netzwerken, im Gegensatz zum Kuramoto-Modell erster Ordnung.Unstetige Phasenübergänge lassen sich anhand eines Ordnungsparameters und der Hysterese auf unterschiedliche Anfangsbedingungen zurückführen. Unter starken Störungen kann das System von wünschenswerten in nicht gewünschte Zustände übergehen. Diese Art der Stabilität unter starken Störungen kann mit dem Konzept der basin stability quantifiziert werden. Im zweiten Teil dieser Dissertation wird die basin stability der Synchronisation im Kuramoto-Modell zweiter Ordnung untersucht, wobei die Knoten separat gestört werden. Dabei wurde ein neues Phänomen mit zwei nacheinander auftretenden Übergängen erster Art entdeckt: Eine \emph{onset transition} von einer globalen Stabilität zu einer lokalen Instabilität, und eine suffusing transition von lokaler zu globaler Stabilität. Diese Abfolge wird als onset and suffusing transition bezeichnet.Die Stabilität von Netzwerknoten kann durch die lokale Netzwerktopologie beeinflusst werden, zum Beispiel haben Knoten neben Netzwerk-Endpunkten eine geringe basin stability. Daraus folgend wird ein neues Konzept der partiellen basin stability vorgeschlagen, insbesondere für cluster synchronization, um die wechselseitigen Stabilitätseinflüsse von Clustern zu quantifizieren.Dieses Konzept wird auf zwei wichtige reale Beispiele angewandt: Neuronale Netzwerke und das nordeuropäische Stromnetzwerk. Die neue Methode erlaubt es instabile und stabile Cluster in neuronalen Netzwerken zu identifizieren und erklärt wie Netzwerk-Endpunkte die Stabilität gefährden. / Synchronization phenomena are ubiquitous in the natural sciences and engineering, but also in social systems. Among the many models that have been proposed for a description of synchronization, the Kuramoto model is most popular. It describes self-sustained phase oscillators rotating at heterogeneous intrinsic frequencies that are coupled through the sine of their phase differences. The second-order Kuramoto model has been used to investigate power grids, Josephson junctions, and other systems.The study of Kuramoto models on networks has recently been boosted because it is simple enough to allow for a mathematical treatment and yet complex enough to exhibit rich phenomena. In particular, explosive synchronization emerges in scale-free networks in the presence of a correlation between the natural frequencies and the network topology. The first main part of this thesis is devoted to study the networked second-order Kuramoto model in the presence of a correlation between the oscillators'' natural frequencies and the network''s degree. The theoretical framework in the continuum limit and for uncorrelated networks is provided for the model with an asymmetrical natural frequency distribution. It is observed that clusters of nodes with the same degree join the synchronous component successively, starting with small degrees. This novel phenomenon is named cluster explosive synchronization. Moreover, this phenomenon is also influenced by the degree mixing in the network connection as shown numerically. In particular, discontinuous transitions emerge not just in disassortative but also in strong assortative networks, in contrast to the first-order model. Discontinuous phase transitions indicated by the order parameter and hysteresis emerge due to different initial conditions. For very large perturbations, the system could move from a desirable state to an undesirable state. Basin stability was proposed to quantify the stability of a system to stay in the desirable state after being subjected to strong perturbations. In the second main part of this thesis, the basin stability of the synchronization of the second-order Kuramoto model is investigated via perturbing nodes separately. As a novel phenomenon uncovered by basin stability it is demonstrated that two first-order transitions occur successively in complex networks: an onset transition from a global instability to a local stability and a suffusing transition from a local to a global stability. This sequence is called onset and suffusing transition.Different nodes could have a different stability influence from or to other nodes. For example, nodes adjacent to dead ends have a low basin stability. To quantify the stability influence between clusters, in particular for cluster synchronization, a new concept of partial basin stability is proposed. The concept is implemented on two important real examples: neural networks and the northern European power grid. The new concept allows to identify unstable and stable clusters in neural networks and also explains how dead ends undermine the network stability of power grids.

In the first part of this thesis, motivated by the development of deep brain stimulation for Parkinson's disease, we consider the problem of reducing the synchrony of a neuronal population via a closed-loop electrical stimulation. This, under the constraints that only the mean membrane voltage of the ensemble is measured and that only one stimulation signal is available (mean-field feedback). The neuronal population is modeled as a network of interconnected Landau-Stuart oscillators controlled by a linear single-input single-output feedback device. Based on the associated phase dynamics, we analyze existence and robustness of phase-locked solutions, modeling the pathological state, and derive necessary conditions for an effective desynchronization via mean-field feedback. Sufficient conditions are then derived for two control objectives: neuronal inhibition and desynchronization. Our analysis suggests that, depending on the strength of feedback gain, a proportional mean-field feedback can either block the collective oscillation (neuronal inhibition) or desynchronize the ensemble.In the second part, we explore two possible ways to analyze related problems on more biologically sound models. In the first, the neuronal population is modeled as the interconnection of nonlinear input-output operators and neuronal synchronization is analyzed within a recently developed input-output approach. In the second, excitability and synchronizability properties of neurons are analyzed via the underlying bifurcations. Based on the theory of normal forms, a novel reduced model is derived to capture the behavior of a large class of neurons remaining unexplained in other existing reduced models.