35 papers
This paper introduces a computationally efficient method combining quantized local reduced-order modeling with adjoint-based optimization to successfully reconstruct trajectories and optimize spatiotemporally chaotic systems, achieving a 3.5-fold speedup over full-order models in a Kuramoto-Sivashinsky variational data assimilation problem.
Using an intermediate-complexity climate model, this study reveals that the Atlantic Meridional Overturning Circulation (AMOC) undergoes a boundary crisis under rising CO levels, where the collapse of its stable state and the resulting long chaotic transients governed by edge states explain large ensemble variances and apparent stochastic bifurcations in Earth system models.
This paper establishes an operational criterion for the emergence of a well-defined global phase in time-dependent oscillator networks, demonstrating that phase robustness depends on the competition between coupling strength and ramp rates, with spectral properties governing synchronization in random networks while topological defects induce persistent partial ordering in spatial lattices.
This paper introduces a novel early warning signal called DE-AC, which estimates the dominant eigenvalue from autocorrelation functions to reliably predict the onset of period-doubling bifurcations in cardiac systems with superior sensitivity and specificity compared to existing indicators.
This study demonstrates that symmetric reservoir topologies significantly enhance prediction accuracy for low-dimensional nonlinear dynamical systems with limited input dimensions, whereas high-dimensional chaotic systems like turbulent shear flow exhibit minimal sensitivity to such structural symmetries.
This paper demonstrates that while a probabilistic totalistic cellular automaton's mean-field formulation approximates the logistic equation only with infinite-range interactions, numerical studies show that shuffling configurations or rewiring a fraction of links (including in small-world networks) effectively emulates logistic behavior and exhibits a bifurcation cascade in density, a phenomenon that also extends to deterministic totalistic automata with similar symmetries.
This paper investigates how linear Ekman friction alters the enstrophy cascade in two-dimensional turbulence by demonstrating that, under high friction, vorticity becomes passively transported, allowing a phenomenological model based on Gaussian-distributed Lagrangian Finite Time Lyapunov Exponents to accurately predict the resulting spectral slope corrections.
This paper develops a renormalization group framework to demonstrate that the phenomenon of spontaneous stochasticity in fluctuating Sabra turbulence models arises from a universal complex fixed point, which governs the slow, oscillatory convergence of solutions to a stochastic process in the ideal limit regardless of specific dissipation or noise types.
This paper introduces Structured Kolmogorov-Arnold Neural ODEs (SKANODEs), a framework that combines structured state-space modeling with Kolmogorov-Arnold Networks to accurately recover interpretable physical latent states and discover compact symbolic governing equations for nonlinear dynamical systems, outperforming black-box neural ODEs and classical identification methods across synthetic and real-world datasets.
This paper demonstrates that while BPS geodesics with enhanced symmetry remain unchanged in the scattering of multiple Abelian-Higgs vortices, the excitation of massive bound modes introduces forces that significantly deform generic vortex trajectories and enhance chaotic behavior in the final state.
This paper demonstrates that linear response theory can break down in uniformly hyperbolic deterministic systems due to hierarchical asymmetry, where progressively finer transport channels activate at decreasing bias levels, causing effective mobility to diverge as the force approaches zero.
This paper analyzes the spatiotemporal stability of synchronized states in coupled map lattices by performing a linear stability analysis of the orbit Jacobian in reciprocal space to determine how coupling strength influences stability against both periodic and incoherent perturbations.
This paper theoretically demonstrates that DC current bias can control chaos in the magnetization dynamics of perpendicular magnetic tunnel junctions, while thermal fluctuations actively induce noise-driven chaotic behavior, offering a foundation for brain-inspired spintronic computing.
This study utilizes exact diagonalization of spectral form factors and power spectra to demonstrate how trapped interacting rotating bosons transition from integrable or pseudo-integrable behavior in moderate interaction regimes to strong quantum chaos consistent with the Gaussian orthogonal ensemble in strong interaction regimes, driven by the interplay between interaction strength and rotational angular momentum.
This paper introduces a Koopman-Krylov framework based on the Koopman-von Neumann description and gEDMD approximation to characterize non-integrable semiclassical string dynamics, demonstrating how integrability-breaking deformations induce observable-dependent delocalization and spectral weight redistribution in Krylov space.