SearcharxivSearch

arXiv · nlin/0105035

Dynamics of the Harper map: Localized states, Cantor spectra and Strange nonchaotic attractors

Abstract

The Harper (or ``almost Mathieu'') equation plays an important role in studies of localization. Through a simple transformation, this equation can be converted into an iterative two dimensional skew--product mapping of the cylinder to itself. Localized states of the Harper system correspond to fractal attractors with nonpositive maximal Lyapunov exponent in the dynamics of the associated Harper map. We study this map and these strange nonchaotic attractors (SNAs) in detail in this paper. The spectral gaps of the Harper system have a unique labeling through a topological invariant of orbits of the Harper map. This labeling associates an integer index with each gap, and the scaling properties of the width of the gaps as a function of potential strength, $ε$ depends on the index. SNAs occur in a large region in parameter space: these regions have a tongue--like shape and end on a Cantor set on the line $ε= 1$ where the states are critically localized, and the spectrum is singular continuous. The SNAs of the Harper map are described in terms of their fractal properties, and the scaling behaviour of their power--spectra. These are created by unusual bifurcations and differ in many respects from SNAs that have hitherto been studied. The technique of studying a quantum eigenvalue problem in terms of the dynamics of an associated mapping can be applied to a number of related problems in 1~dimension. We discuss generalizations of the Harper potential as well as other quasiperiodic potentials in this context.

Explore related subjects

Keep this discovery

BibTeXRIS

Surendra Singh Negi, Ramakrishna Ramaswamy. 2001-05-14. Dynamics of the Harper map: Localized states, Cantor spectra and Strange nonchaotic attractors. https://arxiv.org/abs/nlin/0105035

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Linear Response Predicts Cusp-Pair Births in Networks with a Localized Cubic

Linear response is cheap to measure; the bistability boundaries it organizes are not. For a passive network with one localized cubic, the driving-point receptance $G$ fixes the period-one cusp set at fundamental-harmonic order: cusps lie on a fixed phase contour of $G$, a tangency of that contour under parameter variation creates a pair, and its curvature separates a gap opening from an isolated loop. For a two-mode absorber the linear prediction locates a benchmark birth coupling to $0.3\%$, and to $0.03\%$ once a third-harmonic correction of scale $|G(3\Omega)/G(\Omega)|$ is included.

nlin.CD

Dynamics Creation through Neural Dynamical Transfer Learning

Data-driven machine learning has established a robust foundation for reconstructing nonlinear dynamical systems from observations, primarily for the purposes of forecasting and control. However, most existing efforts focus on recovering specific observed dynamics rather than the generative synthesis of new ones. Inspired by image fusion and style transfer, we introduce a neural network framework termed Neural Dynamical Transfer Learning (NDTL) to create new systems with prescribed dynamics from pairs of parent nonlinear dynamical systems. By computing fundamental dynamical signatures, including the intrinsic dimension, the Kaplan-Yorke dimension, the invariant measure statistics, and the Lyapunov spectrum, we demonstrate that NDTL preserves key features inherited from the parent models while simultaneously generating novel dynamics. Beyond these validation examples, NDTL induces a criterion for dynamics classification, creates stable oscillatory coexistence in the Hastings-Powell food chain model, produces interpretable epidemiological models, and provides a chaotic source for image encryption.

nlin.CD

The Spectral Skeleton of Chaos: Koopman Wave Packets on Poincar\'e Sections

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 approximates spectral measures and constructs finite regularized wave packets. Packet phase supplies a finite-resolution transport coordinate; low modulus marks a singular skeleton where the phase becomes ill-conditioned. We demonstrate the idea on the R\"ossler system, a 32-mode Kuramoto--Sivashinsky Galerkin system, and the forced Duffing oscillator. The packets yield coarse symbolic models on sections ranging from an almost one-dimensional curve to a visibly thick set. Their graphs organize observed low-period orbits and guide targeted searches for others. In Duffing Regime~II, a seven-region rule accounts for $91\%$--$94\%$ of filtered one-step transitions, while failures in the lowest retained modulus decile occur at $5.08$--$5.20$ times the overall rate. The packets are not Koopman eigenfunctions, nor are the regions exact Markov partitions. Together these computations show how spectral information beyond isolated eigenpairs can expose chaotic transport directly from trajectories.

nlin.CD