202 papers
The "Nlin — Cd" category explores the fascinating world of chaotic dynamics in classical systems, where tiny changes in initial conditions can lead to vastly different outcomes. This field investigates how complex behaviors emerge in physical, biological, and engineered systems, offering insights into everything from weather patterns to the stability of mechanical structures. By studying these unpredictable yet deterministic systems, researchers uncover the underlying order within apparent randomness.
On Gist.Science, we process every new preprint in this category directly from arXiv, ensuring you have immediate access to the latest breakthroughs. Our team provides both plain-language overviews and detailed technical summaries, making cutting-edge research on nonlinear dynamics accessible to everyone from students to experts. Below are the latest papers in this dynamic field, complete with our original explanations and analysis.
This paper introduces a novel data-driven approach that infers governing non-autonomous dynamical equations by identifying optimal basis functions, successfully reconstructing and predicting the behavior of diverse real-world systems ranging from biological aggregates to engineering applications.
This paper demonstrates that quantum fluctuations induce the universal melting of classical quasiperiodic limit tori in driven-dissipative Kerr cavities by converting persistent modes into finite-lifetime states through fluctuation-induced dephasing, establishing a distinct non-equilibrium critical phenomenon with observable scaling laws.
This paper investigates the chaotic behavior of Clapp oscillators, proposing a design approach for constructing chaotic Clapp oscillators suitable for chaotic radar applications and verifiable via microstrip technology.
This study mathematically demonstrates that data gaps and outliers significantly compromise the reliability of variance- and autocorrelation-based resilience indicators, with missing values weakening their agreement and outliers causing systematic overestimation of system stability.
This paper introduces the first part of an efficient framework called Approximate Invariant Analysis (AIA) for nonlinear beam dynamics, which combines the construction of approximate invariants with the geometric foundations of the Poincaré rotation number to extract betatron frequencies, as demonstrated using the NSLS-II storage ring.
This paper rigorously demonstrates that state-space systems can accurately learn the long-term statistical "climate" of structurally stable, mixing, or attracting dynamical systems, as their distribution forecasts remain close to the true future distribution over arbitrarily long time horizons even when point forecasts diverge due to sensitivity to initial conditions.
This study investigates the "butterfly effect" in Surface Quasi-Geostrophic turbulence, revealing that infinitesimal localized perturbations exhibit strong variability and undergo a prolonged transient energy decrease before growing, with the duration of this phase depending on the perturbation's initial location.
This paper proposes a novel method combining Fourier analysis and unsupervised clustering of normalized total variations to precisely detect and classify various types of chimera states in networks of coupled Rayleigh oscillators, overcoming the limitations of existing detection techniques.
This paper demonstrates that flow matching on sequential data effectively functions as a memory-augmented, nonparametric dynamical system that replays observed transitions via a similarity-weighted mixture of instantaneous velocities, leading to the development of FreeFM, a training-free sampler that achieves strong probabilistic forecasting directly from historical data.
This article bridges predictive multiplicity and chaotic dynamics by introducing horizon-constrained Rashomon Sets, a framework that describes how model diversity evolves with the prediction horizon in chaotic systems to enable decision-oriented model selection that significantly improves downstream utility in safety-critical applications.