arXiv subjects

Thierry Njougouo

Publications and source records attributed to Thierry Njougouo.

At least 19 recordsLinked to original sources

arXiv2026

Thermal Control of Hysteresis and Deterministic Chaos in a Memristive MEMS Resonator

We investigate the nonlinear dynamics of a thermo-electro-mechanically coupled memristive resonator comprising a doubly clamped Euler--Bernoulli microbeam, an RLC circuit, and a TiO$_2$ memristor with temperature-dependent ionic mobility governed by Mott and Efros--Shklovskii hopping conduction. The dynamics are analyzed using two-dimensional parameter-space maps, bifurcation diagrams, Lyapunov exponents, reconstructed attractors, Poincaré sections, Grassberger--Procaccia correlation-dimension analysis, empirical mode decomposition, the Hilbert--Huang spectrum, and electro-memristive hysteresis. Parameter-space maps reveal predominantly quasi-periodic and deterministic chaotic regimes without stable phase-locked periodic states. Bifurcation analyses show that the beam length and excitation frequency govern the dynamics through the frequency ratio $r_ω=ω_0/ω_b$, whereas the excitation current mainly controls the oscillation amplitude and chaotic intensity. Under fixed operating conditions, the asymptotic regime depends on the initial conditions, and complementary diagnostics identify the thermo-memristive subsystem as the primary source of the nonlinear complexity, subsequently transmitted to the microbeam through electromechanical coupling. Temperature continuously reorganizes the electro-memristive hysteresis through the chain $T \to σ(T) \to M(w,T) \to i_m(t) \to w(t)$. The hysteresis area evolves non-monotonically with temperature, revealing a configuration-dependent optimal thermo-memristive operating point. These findings highlight temperature, beam length, and electrical excitation as complementary control parameters for tailoring thermo-memristive memory, deterministic chaos, and nonlinear dynamics in thermo-active MEMS, with potential applications in neuromorphic sensing and chaos-based secure communication.

nlin.CD↗

arXiv2026

Energy-dynamics interplay in temporal networks triggers explosive synchronization

Synchronization in networks of coupled oscillators is a fundamental problem in the study of collective behavior. In this paper, we investigate the synchronization transition in networks of coupled dynamical systems from an energetic perspective. Interactions between systems/oscillators are assumed to be governed by one of the following mechanisms: (i) the intrinsic energy $\mathbf{H}$, describing the conservative internal dynamics of isolated systems, and (ii) the dissipative energy $\dot{\mathbf{H}}$, accounting for energy losses and exchanges due to interactions and damping. An energetic threshold is introduced to modulate the network connectivity, so that the topology evolves in time according to the instantaneous energetic similarity between systems, allowing us to analyze how the balance between intrinsic and dissipative energy shapes the transition to synchronization. Using the Rössler and Lorenz systems as representative examples, while keeping the framework general and applicable to other dynamical systems, we explore three representative dynamical regimes: periodic, multiperiodic, and chaotic. This reveals that, the nature of the synchronization transition strongly depends on the interplay between microscopic dynamics and the mesoscopic connectivity structure. In particular, chaotic oscillators coupled through intrinsic energy favor explosive synchronization, corresponding to a first-order transition, whereas periodic and multiperiodic dynamics lead to smooth second-order transitions. In contrast, dissipative-energy-based connectivity suppresses first-order transitions in chaotic networks but can induce second-order transition in multiperiodic systems.

nlin.AO↗

arXiv2026

Collective decision-making with higher-order interactions on $d$-uniform hypergraphs

Understanding how group interactions influence opinion dynamics is fundamental to the study of collective behavior. In this work, we propose and study a model of opinion dynamics on $d$-uniform hypergraphs, where individuals interact through group-based (higher-order) structures rather than simple pairwise connections. Each one of the two opinions $A$ and $B$ is characterized by a quality, $Q_A$ and $Q_B$, and agents update their opinions according to a general mechanism that takes into account the weighted fraction of agents supporting either opinion and the pooling error, $α$, a proxy for the information lost during the interaction. Through bifurcation analysis of the mean-field model, we identify two critical thresholds, $α_{\text{crit}}^{(1)}$ and $α_{\text{crit}}^{(2)}$, which delimit stability regimes for the consensus states. These analytical predictions are validated through extensive agent-based simulations on both random and scale-free hypergraphs. Moreover, the analytical framework demonstrates that the bifurcation structure and critical thresholds are independent of the underlying topology of the higher-order network, depending solely on the parameters $d$, i.e., the size of the interaction groups, and the quality ratio. Finally, we bring to the fore a nontrivial effect: the large sizes of the interaction groups, could drive the system toward the adoption of the worst option.

physics.soc-ph↗

arXiv2026

Topological transitions in swarmalators systems

After its development, the swarmalators model attracted a great deal of attention since it was found to be very suitable to reproduce several behaviors in collective dynamics. However, few works explain the transitions that are observed while varying system parameters. In this letter, we demonstrate that the changes observed in swarmalator dynamics are governed by changes in the system's topology. To provide a deeper understanding of these changes, we present a topological framework for the swarmalator system and determine the topological charge $Q$ and the helicity $γ$ of the corresponding topology. Investigations on synchronization and transition to synchronization are studied using this topological charge and the variance of the helicity.

nlin.AO↗

arXiv2026

Characterization of Chaotic and Homogeneous coexisting dynamics of a Memristive Thermo-Controlled MEMS

This work presents the mathematical modeling and numerical investigation of a thermo-controlled Micro-Electro-Mechanical System (MEMS) obtained by coupling an HP memristor with mechanical and electrical resonators. Using the linear drift HP memristor model, the nonlinear electromechanical dynamics are analyzed through Lyapunov exponents, bifurcation diagrams, phase portraits, recurrence plots, Poincaré sections, and Fourier spectra. The results reveal parameter-dependent transitions between quasi-periodic and chaotic oscillations, as well as signatures of coexisting dynamical regimes. A systematic investigation of the intrinsic memristor parameters, namely the ON-state resistance Ron, the OFF-state resistance Roff, the oxide thickness D, and the ionic mobility μ_v, demonstrates that memristive effects strongly influence oscillation amplitudes, resonance frequencies, and nonlinear transitions within the coupled thermo-electro-mechanical system. The state-dependent memristance dynamically modulates the electromechanical coupling and redistributes energy between the electrical and mechanical resonators, thereby generating complex oscillatory responses. In addition, the influence of temperature-sensitive memristive parameters is qualitatively examined through variations of the ionic mobility and resistive states. The results indicate that thermal variations can modify both oscillation amplitudes and dynamical regimes, potentially inducing transitions between quasi-periodic and chaotic behaviors. A comparative discussion with Josephson-junction-based MEMS architectures highlights the operational flexibility and room-temperature compatibility of the HP memristor model for thermo-electro-mechanical applications. These findings suggest promising prospects for adaptive nonlinear oscillators, thermo-sensitive sensors, and chaos-driven electromechanical systems.

nlin.CD↗

arXiv2026

Synchronization of coupled wind turbines

In the context of renewable energies, wind energy appears as a sustainable alternative to address current environmental and energy challenges. This work studies the synchronization and stability of a network of wind turbines subjected to strong disturbances, by integrating a realistic modeling of wind variability by using the Ornstein-Uhlenbeck stochastic process. The dynamics of each wind turbine are described by a Kuramoto-type equation, while synchronization is analyzed through the time evolution of the phases. Stability is studied by analyzing the basin of attraction to the synchronous solution, namely the set of initial conditions leading to the stable synchronous state. Simulations carried out on various models ranging from an isolated wind turbine with constant power to an isolated wind turbine with variable wind power, reveal that the stability of the system is strongly influenced by inertia, damping, wind speed, wind fluctuation rate, correlation time, and coupling strength. Physically, these parameters control the balance between injected mechanical power, energy dissipation, grid-induced restoring forces, and the temporal structure of wind fluctuations, thereby determining the ability of the wind turbine to absorb perturbations and maintain synchronization under fluctuating wind conditions.

physics.soc-ph↗

arXiv2026

GDP-Driven Structural and Dynamical Heterogeneity in the Synchronization of Chaotic Macroeconomic Networks

We investigate the emergence of synchronization in a network of coupled chaotic macroeconomic systems. Each node represents an economy characterized by three key variables savings, gross domestic product (GDP), and foreign capital inflows. These economies interact or are connected through a fitness-based probability that depends on the potential GDP of each node. This formulation allows both structural heterogeneity, arising from uneven network connectivity, and dynamical heterogeneity, due to differences in local parameters, to be explored within a unified framework. Using both numerical simulations and a mean-field approximation, by varying the coupling strength and the degree of heterogeneity of both network topology and dynamical behavior of the nodes, we analyze synchronization transitions. Our results show that the mean-field approach accurately captures the collective dynamics in homogeneous and fully connected networks even with heterogeneity within the intrinsic dynamic of the nodes but fails when strong heterogeneity in the structure of the network is introduced. In heterogeneous networks, the system exhibits partial synchronization and on--off intermittency, where coherent phases of global synchronization alternate with abrupt desynchronization bursts. The distribution of laminar phase durations follows a power-law scaling, consistent with theoretical predictions for intermittent synchronization. From an economic perspective, these results suggest that global business cycle synchronization is inherently fragile: strong integration can promote temporary coordination among economies, but structural and dynamical disparities inevitably lead to intermittent breakdowns of collective behavior.

nlin.AO↗

arXiv2026

Classification of Chimera States via Fourier Analysis and Unsupervised Learning

Chimera states are among the most intriguing phenomena in nonlinear dynamics, characterized by the coexistence of coherent and incoherent behavior in systems of coupled identical oscillators. Many methods have been proposed to detect chimera states and to distinguish their different types. However, such methods often suffer from important limitations that prevent sufficiently precise classification. In this work, we overcome the issue by considering a method based on Fourier analysis to determine key signal characteristics such as amplitude, phase, and frequency, jointly with an unsupervised clustering step acting on normalized total variations, measures of local spatial changes of the above-mentioned dynamical features. The proposed method allows us to identify regions in parameter space returning chimera states, but also to further distinguish between the different types. The method is applied to a network of Rayleigh oscillators, which has been shown to exhibit a rich variety of dynamical patterns.

nlin.PS↗

arXiv2025

Traveling chimeras and collective coordination in beta-cell networks

Pancreatic $β$-cells play a central role in maintaining glucose homeostasis through the pulsatile secretion of insulin. This essential function relies not only on intracellular regulatory mechanisms but also on coordinated interactions among $β$-cells within the islets of Langerhans. Disruptions in this intercellular coordination are increasingly implicated in metabolic disorders such as type~I and type~II diabetes. In this work, we employ a computational framework to investigate the collective dynamics of a network of coupled $β$-cells interacting through a nonlocally coupled ring topology that incorporates both electrical and metabolic coupling pathways. This topology captures short- and long-range interactions known to shape islet communication. Numerical simulations reveal a variety of emergent behaviors, including synchronization, traveling waves, and traveling chimera states, in which coherent and incoherent domains coexist and propagate across the network. These findings provide new insight into the mechanisms governing coordinated $β$-cell activity and the regulation of pulsatile insulin secretion. By clarifying how coupling structure and intercellular communication shape islet-wide dynamics, this work contributes to a deeper understanding of the dysfunctions underlying diabetes.

physics.bio-ph↗

arXiv2025

Mobile oscillators in a mobile multi-cluster network

Different collective behaviors emerging from the unknown have been examined in networks of mobile agents in recent years. Mobile systems, far from being limited to modeling and studying various natural and artificial systems in motion and interaction, offer versatile solutions across various domains, facilitating tasks ranging from navigation and communication to data collection and environmental monitoring. We examine the relative mobility between clusters, each composed of different elements in a multi-clusters network-a system composed of clusters interconnected to form a larger network of mobile oscillators. Each mobile oscillator exhibits both external (i.e., position in a 2D space) and internal dynamics (i.e., phase oscillations). Studying the mutual influence between external and internal dynamics, often leads the system towards a state of synchronization within and between clusters. We show that synchronization between clusters is affected by their spatial closeness. The stability of complete synchronization observed within the clusters is demonstrated through analytical and numerical methods.

nlin.AO↗

arXiv2025

Attractive-repulsive challenge in swarmalators with time-dependent speed

We examine a network of entities whose internal and external dynamics are intricately coupled, modeled through the concept of ``swarmalators'' as introduced by O'Keeffe et al. \textcolor{blue}{\cite{o2017oscillators}}. We investigate how the entities' natural velocities impact the network's collective dynamics and path to synchronization. Specifically, we analyze two scenarios: one in which each entity has an individual natural velocity, and another where a group velocity is defined by the average of all velocities. Our findings reveal two distinct forms of phase synchronization -- static and rotational -- each preceded by a complex state of attractive-repulsive interactions between entities. This interaction phase, which depends sensitively on initial conditions, allows for selective modulation within the network. By adjusting initial parameters, we can isolate specific entities to experience attractive-repulsive interactions distinct from the group, prior to the onset of full synchronization. This nuanced dependency on initial conditions offers valuable insights into the role of natural velocities in tuning synchronization behavior within coupled dynamic networks.

nlin.AO↗

arXiv2025

Chimera state in neural network with the Proportional-Integral-Derivative coupling

This study delves into the emergence of collective behaviors within a network comprising interacting cells. Each cell integrates a fixed number of neurons governed by an activation gradient based on Hopfield's model. The intra-cell interactions among neurons are local and directed, while inter-cell connections are facilitated through a PID (Proportional-Integral-Derivative) coupling mechanism. This coupling introduces an adaptable environmental variable, influencing the network dynamics significantly. Numerical simulations employing three neurons per cell across a network of fifty cells reveal diverse dynamics, including incoherence, coherence, synchronization, chimera states, and traveling wave. These phenomena are quantitatively assessed using statistical measures such as the order parameter, strength of incoherence, and discontinuity measure. Variations of the resistive, inductive, or capacitive couplings of the inter-cell environment are explored and their effects are analysed. Furthermore, the study identifies multistability in network dynamics, characterized by the coexistence of multiple stable states for the same set of parameters but with different initial conditions. A linear augmentation strategy is employed for its control.

nlin.PS↗

arXiv2024

Expected and unexpected routes to synchronization in a system of swarmalators

Systems of oscillators whose internal phases and spatial dynamics are coupled, swarmalators, present diverse collective behaviors which in some cases lead to explosive synchronization in a finite population as a function of the coupling parameter between internal phases. Near the synchronization transition, the phase energy of the particles is represented by the XY model, and they undergo a transition which can be of the first order or second depending on the distribution of natural frequencies of their internal dynamics. The first order transition is obtained after an intermediate state (Static Wings Phase Wave state (SWPW)) from which the nodes, in cascade over time, achieve complete phase synchronization at a precise value of the coupling constant. For a particular case of natural frequencies distribution, a new phenomenon of Rotational Splintered Phase Wave state (RSpPW) is observed and leads progressively to synchronization through clusters switching alternatively from one to two and for which the frequency decreases as the phase coupling increases.

nlin.AO↗

arXiv2024

Studying speed-accuracy trade-offs in best-of-n collective decision-making through heterogeneous mean-field modeling

To succeed in their objectives, groups of individuals must be able to make quick and accurate collective decisions on the best option among a set of alternatives with different qualities. Group-living animals aim to do that all the time. Plants and fungi are thought to do so too. Swarms of autonomous robots can also be programmed to make best-of-n decisions for solving tasks collaboratively. Ultimately, humans critically need it and so many times they should be better at it. Thanks to their mathematical tractability, simple models like the voter model and the local majority rule model have proven useful to describe the dynamics of such collective decision-making processes. To reach a consensus, individuals change their opinion by interacting with neighbors in their social network. At least among animals and robots, options with a better quality are exchanged more often and therefore spread faster than lower-quality options, leading to the collective selection of the best option. With our work, we study the impact of individuals making errors in pooling others' opinions caused, for example, by the need to reduce the cognitive load. Our analysis is grounded on the introduction of a model that generalizes the two existing models (local majority rule and voter model), showing a speed-accuracy trade-off regulated by the cognitive effort of individuals. We also investigate the impact of the interaction network topology on the collective dynamics. To do so, we extend our model and, by using the heterogeneous mean-field approach, we show the presence of another speed-accuracy trade-off regulated by network connectivity. An interesting result is that reduced network connectivity corresponds to an increase in collective decision accuracy.

physics.soc-ph↗

arXiv2024

Phase chimera states on non-local hyperrings

Chimera states are dynamical states where regions of synchronous trajectories coexist with incoherent ones. A significant amount of research has been devoted to study chimera states in systems of identical oscillators, non-locally coupled through pairwise interactions. Nevertheless, there is an increasing evidence, also supported by available data, that complex systems are composed by multiple units experiencing many-body interactions, that can be modeled by using higher-order structures beyond the paradigm of classic pairwise networks. In this work we investigate whether phase chimera states appear in this framework, by focusing on a novel topology solely involving many-body, non-local and non-regular interactions, hereby named non-local d-hyperring, being (d+1) the order of the interactions. We present the theory by using the paradigmatic Stuart-Landau oscillators as node dynamics, and show that phase chimera states emerge in a variety of structures and with different coupling functions. For comparison, we show that, when higher-order interactions are "flattened" to pairwise ones, the chimera behavior is weaker and more elusive.

nlin.PS↗

arXiv2023

On the role of zealots in a best-of-n problem on a heterogeneous network

Both humans and social animals live in groups and are frequently faced to choose between options with different qualities. When no leader agents are controlling the group decision, consensus can be achieved through repeated interactions among group members. Various studies on CDM illustrate how the dynamics of opinions are determined by the structure of the social network and the methods that individuals use to share and update their opinion upon a social interaction. In this paper, we are interested in further exploring how cognitive, social, and environmental factors interactively contribute to determining the outcome of a collective best-of-n decision process involving asymmetric options, i.e., different costs and/or benefits for each option. We propose and study a novel model capturing those different factors, i) the error in processing social information, ii) the number of zealots (i.e., asocial agents who never change their opinion), iii) the option qualities, iv) the social connectivity structure, and v) the degree centrality of the asocial agents. By using the HMF approach, we study the impact of the above-mentioned factors in the decision dynamics. Our findings indicate that when susceptible agents use the voter model as a mechanism to update their opinion, both the number and the degree of connectivity of the zealots can lead the population to converge towards the lowest quality option. Instead, when susceptible agents use methods more cognitively demanding, the group is marginally impacted by the presence of zealots. The results of the analytical model are complemented and extended by agent-based simulations. Our analysis also shows that the network topology can modulate the influence of zealots on group dynamics.

physics.soc-ph↗

arXiv2022

Synchronization in a multilevel network using the Hamilton-Jacobi-Bellman (HJB) technique

This paper presents the optimal control and synchronization problem of a multilevel network of Rössler chaotic oscillators. Using the Hamilton-Jacobi-Bellman (HJB) technique, the optimal control law with three-state variables feedback is designed such that the trajectories of all the Rössler oscillators in the network are optimally synchronized in each level. Furthermore, we provide numerical simulations to demonstrate the effectiveness of the proposed approach for the cases of one and three networks. A perfect correlation between the MATLAB and the PSPICE results was obtained, thus allowing the experimental validation of our designed controller and shows the effectiveness of the theoretical results.

math.OC↗

arXiv2021

Chimera states in a neuronal network under the action of an electric field

The phenomenon of the chimera state symbolizes the coexistence of coherent and incoherent sections of a given population. This phenomenon identified in several physical and biological systems presents several variants, including the multichimera states and the traveling chimera state. Here, we numerically study the influence of a weak external electric field on the dynamics of a network of Hindmarsh-Rose (HR) neurons coupled locally by an electrical interaction and nonlocally by a chemical one. We first focus on the phenomena of traveling chimera states and multicluster oscillating breathers that appear in the electric field's absence. Then in the field's presence, we highlight the presence of a chimera state, a multichimera state, an alternating chimera state and a multicluster traveling chimera.

nlin.AO↗