21 papers
This paper proposes and analyzes a simple mathematical model describing how active and passive particles connected by a linear spring exhibit distinct straight, circular, and slalom motions, with theoretical analysis confirming a bifurcation between straight and circular trajectories driven by the magnitude of self-propulsion.
This paper reviews deterministic and stochastic network-level models to explain how Arrhenius-like temperature dependencies in individual biochemical reactions transform into complex emergent system behaviors, such as non-Arrhenius scaling and thermal limits, thereby bridging empirical temperature response curves with the molecular organization of biological systems.
This review article surveys phenomenological and microscopic models used to describe the complex, non-Arrhenius temperature responses of biological systems across various scales, defining key operational metrics like optimal temperatures and thermal limits while setting the stage for a subsequent discussion on how system-level curves emerge from interacting reactions.
This paper demonstrates through both numerical simulations and physical experiments that two coupled identical chaotic Lorenz oscillators exhibit simultaneous deterministic coherence and anti-coherence resonances in their respective state variables when the coupling strength is below the threshold for complete synchronization, a regime characterized by hyperchaotic dynamics and on-off intermittency.
GradNet introduces a gradient-based, AI-enabled framework that inverts traditional network science by treating topology as a continuously differentiable object to optimize arbitrary dynamical objectives under constraints, thereby demonstrating that canonical network features emerge spontaneously from constrained optimization rather than explicit design.
This paper proposes a d-dimensional generalization of the Kuramoto model on matrix-weighted networks, deriving necessary conditions for global synchronization and proving via a Master Stability Function approach that the synchronous solution is locally stable for any positive coupling strength on connected networks.
This paper identifies a novel mechanism called "pseudo-coherence," where non-normal pseudospectral amplification in linearly stable, non-oscillatory stochastic systems drives a sharp transition to intermittent collective temporal organization and irreversible currents without requiring intrinsic oscillators or dynamical instabilities.
This paper analyzes a one-dimensional model of a self-propelled camphor disk perturbed by a second localized source, demonstrating through both numerical simulations and analytical solutions that the rotor's velocity exhibits a pronounced asymmetry depending on whether it approaches or recedes from the perturbation.
This paper presents a mathematical model using replicator dynamics to demonstrate how social feedback mechanisms—specifically positive versus negative feedback—govern whether a society undergoes a discontinuous or continuous phase transition between widespread compliance and rule-breaking, thereby explaining the fragility of social order under weak institutions.
This study demonstrates that adaptive pendulum networks governed by Hebbian or STDP learning rules spontaneously generate diverse collective states, including a novel delay-free solitary state driven purely by phase-lag variations, and systematically maps these dynamical transitions using incoherence measures and stability analysis.
This paper investigates a pulse-coupled adaptive Winfree network with a frustration parameter, revealing that Hebbian adaptation spontaneously generates diverse collective states—including entrainment, bump, and chimera states without external forcing—and systematically characterizes these dynamics through new incoherence measures and analytical stability conditions.
This paper presents a framework for identifying and analyzing dynamics-induced active-inactive cluster patterns in complex networks that arise from symmetry breaking in systems with odd intrinsic dynamics and coupling, demonstrating that such patterns can exist without requiring network symmetries and providing a stability analysis based on coupling strength and intercluster weights.
This paper establishes rigorous Lyapunov and port-Hamiltonian stability conditions for chaotic synchronization in a 5D memristive Hindmarsh-Rose neuron model and introduces a novel physics-informed neural network that successfully learns the underlying synchronization Hamiltonian while preserving its conservative and dissipative structures.
This study demonstrates that explosive synchronization can emerge in networks of Type-I neurons (specifically Quadratic Integrate-and-Fire and Morris-Lecar models) coupled by electrical synapses on scale-free and star topologies, provided there is a correlation between node degree and natural frequency under conditions of weak heterogeneity.
This paper identifies three qualitatively distinct noise-induced escape mechanisms in populations of diffusively coupled bistable elements by developing a model-reduction approach that derives specific effective dynamics for weak, intermediate, and strong coupling regimes, revealing that these mechanisms arise from the interplay of nonlinearity, coupling, and noise rather than bifurcations of the noise-free system.
This study introduces a topological analysis based on winding numbers to reveal that spiral wave chimera states undergo a physical crossover from geometric core expansion to active topological excitation, characterized by distinct scaling laws and a statistical transition in defect distribution as the phase lag varies.
This paper develops a phase reduction method for spatially extended reaction-diffusion systems with discrete delays by introducing a tailored bilinear form to derive phase sensitivity functions, which is numerically verified and applied to optimize synchronization stability in the Schnakenberg system.
This paper numerically investigates a ring network of phase oscillators with frequency heterogeneity and phase lag, revealing how these factors influence the basin sizes of multistable synchronous states and proposing a control method to steer the system toward specific wavenumbers.
This paper analyzes coupled phase oscillators with turnover to reveal two distinct transitions—desynchronization and a newly identified phenomenon called stochastic oscillation quenching, the latter of which requires sufficiently intense coupling and turnover.
By applying the Ott-Antonsen ansatz and numerical simulations to a matrix-coupled Kuramoto model driven by an external periodic force, this study demonstrates that such a system exhibits multiple resonant modes and complex Arnold tongues, contrasting with the original model where only 1:1 resonance occurs.