Classical ordinal-pattern methods quantify complexity from a scalar time series without requiring a model, but they are usually reported as global statistics and say little about where along a chaotic trajectory ordinal regimes change. For a smooth flow x˙=F(x) observed through a scalar ϕ(x), we define the first two material derivatives ϕ˙=LFϕ and ϕ¨=LF2ϕ and the associated organizer sets M1(ϕ)={x:ϕ˙=0} and M0(ϕ)={x:ϕ¨=0}. An exact integral criterion shows that if LFϕ keeps a strict sign on an ordinal window, then the corresponding ordinal pattern is monotone; thus, M1 marks where that sufficient sign-persistence mechanism can fail, while M1∩M0 identifies tangencies of M1 at its regular points. A small-delay expansion further shows that ordinal ranks in delay embeddings are controlled at leading orders by (ϕ˙,ϕ¨). We quantify organizer effects with an ordinal sensitivity index (OSI), defined as the Jensen-Shannon divergence between ordinal-pattern distributions in near and far sets. For Lorenz-63 with ϕ = x, we derive a closed-form expression for M0(x). Across Lorenz-63 and the Aizawa system, OSI remains large over broad embedding and delay ranges; near-M1 conditioning increases switching and transition entropy in 98.8%-100% of tested configurations, whereas M0 produces strong but delay-dependent separation and often increased monotone-pattern prevalence. As a secondary delay-space refinement, a boundary-clearance score improves switching prediction beyond |ϕ˙| in all tested configurations.
We develop a structural framework for irregularity in linear dynamics centered on the space $\Upsilon$ of observation schemes. This approach separates the underlying dynamical behavior from the observation mechanism and provides a unified setting in which classical notions such as Li--Yorke chaos, mean Li--Yorke chaos,...
Chaotic dynamical systems, known for their extreme sensitivity to initial conditions, present a significant challenge for accurate parameter estimation. This paper introduces a robust Objective Function (OF) that uses the invariant topological properties of the system’s attractor, derived from Recurrence Plots (RPs), t...
Nafise Naseri, Mohammad Bagher Khodabakhshi, Farnaz Ghassemi et al.· International Journal of Bif...· 0 citations
Largest Lyapunov exponents (LLEs) quantify exponential sensitivity, but data-driven estimates are often obtained from finite-precision trajectories. We show that increasing the length of a single reduced-precision chaotic record can eventually degrade a forecast-error LLE estimate. Using the logistic map at r=4, an ESP...
A Poincar\'e section replaces a flow by a return map, but for a chaotic system this map is usually known only from sampled crossings. We show that coarse transport can be read directly from Koopman spectral data, without fitting the map. Measure-preserving EDMD retains the isometric structure; riggedDMD then approximat...
Jason J. Bramburger, Shiloh-Sheray Gagnon, Rachel Morris et al.· 0 citations
Integer maps are discrete dynamical systems defined on the natural numbers. In this paper, we investigate the dynamics of the shifted Alladi-Erd{\H o}s map, a one-parameter family of integer maps in which each composite number is mapped to the sum of its prime factors, while each prime is mapped to $n+A$, where $A \in...
This study presents a three-dimensional chaotic model whose entire nonlinearity consists of two exponential terms and which contains no product of state variables. The two exponentials are crosscoupled, each entering the equation of the other variable, while the third equation remains linear. Because no term contains...
Muhammad Ali Qureshi, Syed Mohsin Abbas Naqvi, S. Ijlal et al.· Engineering Research Express· 0 citations
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