CyberSec Research

We identify a new class of time periodic attractor solutions in scalar field cosmology, which we term Cosmological Frequency Combs (CFC). These solutions arise in exponential quintessence models with a phantom matter background and exhibit coherent phase-locked oscillations in the scalar field's normalized variables. We demonstrate that such dynamics induce modulations in observables like the Hubble parameter and growth rate, offering a dynamical mechanism to even address the H0 tension exactly. Our results uncover a previously unexplored phase of cosmic acceleration, linking the concept of frequency combs to large scale cosmological evolution.

We investigate the transition to synchronization in adaptive multilayer networks with higher-order interactions both analytically and numerically in the presence of phase frustration ($\beta$). The higher order topology consists of pairwise and triadic couplings. The analytical framework for the investigation is based on the Ott-Antonsen ansatz which leads to a convenient low-dimensional model. Extensive bifurcation analysis of the low-dimensional model and the numerical simulation of the full networks are performed to explore the paths to synchronization. The combined analysis shows a complex dependence of the transition to synchronization on adaptation exponents, coupling strengths, phase lag parameter, and multilayer configuration. Various types of transitions to synchronization, namely continuous, tiered, and explosive, are exhibited by the system in different regions of the parameter space. In all the cases, a satisfactory match between the low-dimensional model and the numerical simulation results is observed. The origin of different transitions to synchronization is clearly understood using the low-dimensional model. Exploration of a wide region of the parameter space suggests that the phase frustration parameter inhibits tired as well as explosive synchronization transitions for fixed triadic coupling strength ($K_2$). On the other hand, discontinuous transition is promoted by the phase frustration parameter for fixed pairwise coupling strength ($K_1$). Moreover, the exponent of the adaptation function with the pairwise coupling decreases the width of the hysteresis, despite the dominance of the higher-order coupling for fixed $\beta$ and $K_2$. While, the exponent of the function adapted with higher-order coupling shows the opposite effect, it promotes bistability in spite of dominance of pairwise coupling strength for fixed $\beta$, and $K_1$.

We theoretically analyze the nonlinear dynamics and routes to chaos in a broad-area vertical cavity surface-emitting laser (BA-VCSEL) in free-running operation. Accounting for the onset of higher order transverse modes (TMs) unveils additional bifurcations at higher currents not found for single-mode VCSELs (SM-VCSELs). The resulting dynamics involves competition between polarization modes with different transverse profiles and shows good qualitative agreement with recent experimental observations.

In this paper we present Chaoticus, a Python-based package for the GPU-accelerated integration of ODE systems and the computation of chaos indicators, including SALI, GALI, Lagrangian Descriptors based indicators and the Lyapunov exponent spectrum. By leveraging GPU parallelization, our package significantly reduces the computation times by several orders of magnitude compared to CPU-based approaches. This significant reduction in computing time facilitates the generation of extensive datasets, crucial for the in-depth analysis of complex dynamics in Hamiltonian systems.

We study Ruelle-Pollicott resonances of translationally invariant magnetization-conserving qubit circuits via the spectrum of the quasi-momentum-resolved truncated propagator of extensive observables. Diffusive transport of the conserved magnetization is reflected in the Gaussian quasi-momentum $k$ dependence of the leading eigenvalue (Ruelle-Pollicott resonance) of the truncated propagator for small $k$. This, in particular, allows us to extract the diffusion constant. For large $k$, the leading Ruelle-Pollicott resonance is not related to transport and governs the exponential decay of correlation functions. Additionally, we conjecture the existence of a continuum of eigenvalues below the leading diffusive resonance, which governs non-exponential decay, for instance, power-law hydrodynamic tails. We expect our conclusions to hold for generic systems with exactly one U(1) conserved quantity.

Many parts of the Earth system are thought to have multiple stable equilibrium states, with the potential for rapid and sometimes catastrophic shifts between them. The most common frameworks for analyzing stability changes, however, require stationary (trend- and seasonality-free) data, which necessitates error-prone data pre-processing. Here we propose a novel method of quantifying system stability based on eigenvalue tracking and Floquet Multipliers, which can be applied directly to diverse data without first removing trend and seasonality, and is robust to changing noise levels, as can be caused by merging signals from different sensors. We first demonstrate this approach with synthetic data and further show how glacier surge onset can be predicted from observed surface velocity time series. We then show that our method can be extended to analyze spatio-temporal data and illustrate this flexibility with remotely sensed Amazon rainforest vegetation productivity, highlighting the spatial patterns of whole-ecosystem destabilization. Our work applies critical slowing down theory to glacier dynamics for the first time, and provides a novel and flexible method to quantify the stability or resilience of a wide range of spatiotemporal systems, including climate subsystems, ecosystems, and transient landforms.

In general terms, intermittency is the property for which time evolving systems alternate among two or more different regimes. Predicting the instance when the regime switch will occur is extremely challenging, often practically impossible. Intermittent processes include turbulence, convection, precipitation patterns, as well as several in plasma physics, medicine, neuroscience, and economics. Traditionally, focus has been on global statistical indicators, e.g. the average frequency of regime changes under fixed conditions, or how these vary as a function of the system's parameters. We add a local perspective: we study the causes and drivers of the regime changes in real time, with the ultimate goal of predicting them. Using five different systems, of various complexities, we identify indicators and precursors of regime transitions that are common across the different intermittency mechanisms and dynamical models. For all the systems and intermittency types under study, we find a correlation between the alignment of some Lyapunov vectors and the concomitant, or aftermath, regime change. We discovered peculiar behaviors in the Lorenz 96 and in the Kuramoto-Shivanshinki models. In Lorenz 96 we identified crisis-induced intermittency with laminar intermissions, while in the Kuramoto-Shivanshinki we detected a spatially global intermittency which follows the scaling of type-I intermittency. The identification of general mechanisms driving intermittent behaviors, and in particular the unearthing of indicators spotting the regime change, pave the way to designing prediction tools in more realistic scenarios. These include turbulent geophysical fluids, rainfall patterns, or atmospheric deep convection.

The information scrambling phenomena in an open quantum system modeled by Ising spin chains coupled to Lipkin-Meshkov-Glick (LMG) baths are observed via an interferometric method for obtaining out-of-time-ordered correlators ($\mathcal{F}-$OTOC). We also use an anisotropic bath connecting to a system of tilted field Ising spin chain in order to confirm that such situations are suitable for the emergence of ballistic spreading of information manifested in the light cones in the $\mathcal{F}-$OTOC profiles. Bipartite OTOC is also calculated for a bipartite open system, and its behavior is compared with that of the $\mathcal{F}-$OTOC of a two-spin open system to get a picture of what these measures reveal about the nature of scrambling in different parameter regimes. Additionally, the presence of distinct phases in the LMG model motivated an independent analysis of its scrambling properties, where $\mathcal{F}-$OTOC diagnostics revealed that quantum chaos emerges exclusively in the symmetry-broken phase.

We investigate diffusion in a two-dimensional inverted soft Lorentz gas, where attractive Fermi-type potential wells are arranged in a triangular lattice. This configuration contrasts with earlier studies of soft Lorentz gases involving repulsive scatterers. By systematically varying the gap width and softness of the potential, we explore a rich landscape of diffusive behaviors. We present numerical simulations of the mean squared displacement and compute diffusion coefficients, identifying tongue-like structures in parameter space associated with quasiballistic transport. Furthermore, we develop an extension to the Machta-Zwanzig approximation that incorporates correlated multi-hop trajectories and correct for the influence of localized periodic orbits. Our findings highlight the qualitative and quantitative differences between inverted and repulsive soft Lorentz gases and offer new insights into transport phenomena in smooth periodic potentials.

We investigate the dynamical properties of cusp bifurcations in max-plus dynamical systems derived from continuous differential equations through the tropical discretization and the ultradiscrete limit. A general relationship between cusp bifurcations in continuous and corresponding discrete systems is formulated as a proposition. For applications of this proposition, we analyze the Ludwig and Lewis models, elucidating the dynamical structure of their ultradiscrete cusp bifurcations obtained from the original continuous models. In the resulting ultradiscrete max-plus systems, the cusp bifurcation is characterized by piecewise linear representations, and its behavior is examined through the graph analysis.

Large-scale neural mass models have been widely used to simulate resting-state brain activity from structural connectivity. In this work, we extend a well-established Wilson--Cowan framework by introducing a novel hemispheric-specific coupling scheme that differentiates between intra-hemispheric and inter-hemispheric structural interactions. We apply this model to empirical cortical connectomes and resting-state fMRI data from matched control and schizophrenia groups. Simulated functional connectivity is computed from the band-limited envelope correlations of regional excitatory activity and compared against empirical functional connectivity matrices. Our results show that incorporating hemispheric asymmetries enhances the correlation between simulated and empirical functional connectivity, highlighting the importance of anatomically-informed coupling strategies in improving the biological realism of large-scale brain network models.

This work explores key conceptual limitations in data-driven modeling of multiscale dynamical systems, focusing on neural emulators and stochastic climate modeling. A skillful climate model should capture both stationary statistics and responses to external perturbations. While current autoregressive neural models often reproduce the former, they typically struggle with the latter. We begin by analyzing a low-dimensional dynamical system to expose, by analogy, fundamental limitations that persist in high-dimensional settings. Specifically, we construct neural stochastic models under two scenarios: one where the full state vector is observed, and another with only partial observations (i.e. a subset of variables). In the first case, the models accurately capture both equilibrium statistics and forced responses in ensemble mean and variance. In the more realistic case of partial observations, two key challenges emerge: (i) identifying the \textit{proper} variables to model, and (ii) parameterizing the influence of unobserved degrees of freedom. These issues are not specific to neural networks but reflect fundamental limitations of data-driven modeling and the need to target the slow dynamics of the system. We argue that physically grounded strategies -- such as coarse-graining and stochastic parameterizations -- are critical, both conceptually and practically, for the skillful emulation of complex systems like the coupled climate system. Building on these insights, we turn to a more realistic application: a stochastic reduced neural model of the sea surface temperature field and the net radiative flux at the top of the atmosphere, assessing its stationary statistics, response to temperature forcing, and interpretability.

We show that recurrent quantum reservoir computers (QRCs) and their recurrence-free architectures (RF-QRCs) are robust tools for learning and forecasting chaotic dynamics from time-series data. First, we formulate and interpret quantum reservoir computers as coupled dynamical systems, where the reservoir acts as a response system driven by training data; in other words, quantum reservoir computers are generalized-synchronization (GS) systems. Second, we show that quantum reservoir computers can learn chaotic dynamics and their invariant properties, such as Lyapunov spectra, attractor dimensions, and geometric properties such as the covariant Lyapunov vectors. This analysis is enabled by deriving the Jacobian of the quantum reservoir update. Third, by leveraging tools from generalized synchronization, we provide a method for designing robust quantum reservoir computers. We propose the criterion $GS=ESP$: GS implies the echo state property (ESP), and vice versa. We analytically show that RF-QRCs, by design, fulfill $GS=ESP$. Finally, we analyze the effect of simulated noise. We find that dissipation from noise enhances the robustness of quantum reservoir computers. Numerical verifications on systems of different dimensions support our conclusions. This work opens opportunities for designing robust quantum machines for chaotic time series forecasting on near-term quantum hardware.

The Kuramoto model, a paradigmatic framework for studying synchronization, exhibits a transition to collective oscillations only above a critical coupling strength in the thermodynamic limit. However, real-world systems are finite, and their dynamics can deviate significantly from mean-field predictions. Here, we investigate finite-size effects in the Kuramoto model below the critical coupling, where the infinite-size theory predicts complete asynchrony. Using a shot-noise approach, we derive analytically the power spectrum of emergent collective oscillations and demonstrate their dependence on the coupling strength. Numerical simulations confirm our theoretical results, though deviations arise near the critical coupling due to nonlinear effects. Our findings reveal how finite-size fluctuations sustain synchronization in regimes where classical mean-field theories fail, offering insights for applications in neural networks, power grids, and other coupled oscillator systems.

Simulating nonlinear classical dynamics on a quantum computer is an inherently challenging task due to the linear operator formulation of quantum mechanics. In this work, we provide a systematic approach to alleviate this difficulty by developing a quantum algorithm that implements the time evolution of a second order time-discretized version of the Lorenz model. The Lorenz model is a celebrated system of nonlinear ordinary differential equations that has been extensively studied in the contexts of climate science, fluid dynamics, and chaos theory. Our algorithm possesses a recursive structure and requires only a linear number of copies of the initial state with respect to the number of integration time-steps. This provides a significant improvement over previous approaches, while preserving the characteristic quantum speed-up in terms of the dimensionality of the underlying differential equations system, that similar time-marching quantum algorithms have previously demonstrated. Notably, by classically implementing the proposed algorithm, we showcase that it accurately captures the structural characteristics of the Lorenz system, reproducing both regular attractors--limit cycles--and the chaotic attractor within the chosen parameter regime.

The Looping pendulum phenomenon was first introduced in 2019 at the 32nd edition of the IYPT, wherein a lighter bob sweeps around a cylindrical rod to support the weight of a heavier bob. In this paper, the phenomenon was divided based on rotating and non-rotating forces, and differential equations were derived for each. To verify the theoretical derivation, an experimental analysis was done, varying the mass ratio with the vertical distance travelled by the heavier bob. (Tracked using tracker) Experimental findings fit a logarithmic curve fit -- falling succinctly with a similar trend with the simulation run with MATLAB solving the derived differential equations. Furthermore, to verify the simulation, the trajectory of both the lighter and heavier mass was also compared for the simulation and experimental findings. The experimental findings fit very closely to the simulation findings, accrediting the validity and accuracy of the derived theory.

Active nematic systems consist of rod-like internally driven subunits that interact with one another to form large-scale coherent flows. They are important examples of far-from-equilibrium fluids, which exhibit a wealth of nonlinear behavior. This includes active turbulence, in which topological defects braid around one another in a chaotic fashion. One of the most studied examples of active nematics is a dense two-dimensional layer of microtubules, crosslinked by kinesin molecular motors that inject extensile deformations into the fluid. Though numerous studies have modeled microtubule-based active nematics, no consensus has emerged on how to fully capture the features of the experimental system. To better understand the foundations for modeling this system, we propose a fundamental principle we call the nematic locking principle: individual microtubules cannot rotate without all neighboring microtubules also rotating. Physically, this is justified by the high density of the microtubules, their elongated nature, and their corresponding steric interactions. We assert that nematic locking holds throughout the majority of the material but breaks down in the neighborhood of topological defects and other regions of low density. We derive the most general nematic transport equation consistent with this principle and also derive the most general term that violates it. We examine the standard Beris-Edwards approach used to model this system and show that it violates nematic locking throughout the majority of the material. We then propose a modification to the Beris-Edwards model that enforces nematic locking nearly everywhere. This modification shuts off fracturing except in regions where the order parameter is reduced. The resulting simulations show strong nematic locking throughout the bulk of the material, consistent with experimental observation.

Flutter suppression facilitates the improvement of structural reliability to ensure the flight safety of an aircraft. In this study, we propose a novel strategy for enlarging amplitude death (AD) regime to enhance flutter suppression in two coupled identical airfoils with structural nonlinearity. Specifically, we introduce an intermittent mixed coupling strategy, i.e., a linear combination of intermittent instantaneous coupling and intermittent time-delayed coupling between two airfoils. Numerical simulations are performed to reveal the influence mechanisms of different coupling scenarios on the dynamical behaviors of the coupled airfoil systems. The obtained results indicate that the coupled airfoil systems experience the expected AD behaviors within a certain range of the coupling strength and time-delayed parameters. The continuous mixed coupling favors the onset of AD over a larger parameter set of coupling strength than the continuous purely time-delayed coupling. Moreover, the presence of intermittent interactions can lead to a further enlargement of the AD regions, that is, flutter suppression enhancement. Our findings support the structural design and optimization of an aircraft wing for mitigating the unwanted aeroelastic instability behaviors.