Guo-Qiang Wu | Searcharxiv

arXiv2010

Singular Perturbation of Nonlinear Dynamics by Parasitic Noise

In nonlinear systems analysis, minor fractions of higher-order dynamics are often neglected for simplicity. Here, we show that machine epsilon levels of parasitic higher-order dynamics due to computer roundoff alone can cause divergence of the Hénon attractor to new attractors or instability. The divergence develops exponentially regardless of whether the original or new attractor is chaotic or not. Such singular perturbation by parasitic higher-order dynamics is a novel property of nonlinear dynamics that is of wide practical significance in dynamical systems modeling, simulation and control.

physics.comp-ph↗

arXiv2010

A unified theory of chaos linking nonlinear dynamics and statistical physics

A fundamental issue in nonlinear dynamics and statistical physics is how to distinguish chaotic from stochastic fluctuations in short experimental recordings. This dilemma underlies many complex systems models from stochastic gene expression or stock exchange to quantum chaos. Traditionally, deterministic chaos is characterized by "sensitive dependence on initial conditions" as indicated by a positive Lyapunov exponent. However, ambiguity arises when applying this criterion to real-world data that are corrupted by measurement noise or perturbed nonautonomously by exogenous deterministic or stochastic inputs. Here, we show that a positive Lyapunov exponent is surprisingly neither necessary nor sufficient proof of deterministic chaos, and that a nonlinear dynamical system under deterministic or stochastic forcing may exhibit multiple forms of nonautonomous chaos assessable by a noise titration assay. These findings lay the foundation for reliable analysis of low-dimensional chaos for complex systems modeling and prediction of a wide variety of physical, biological, and socioeconomic data.

nlin.CD↗