Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2605.29614

Characterization of Chaotic and Homogeneous coexisting dynamics of a Memristive Thermo-Controlled MEMS

N. G. Koudafokê·Thierry Njougouo·Hilda A. Cerdeira·C. H. Miwadinou
View original source ↗Read source document ↗Export metadata ↓

Abstract

This work presents the mathematical modeling and numerical investigation of a thermo-controlled Micro-Electro-Mechanical System (MEMS) obtained by coupling an HP memristor with mechanical and electrical resonators. Using the linear drift HP memristor model, the nonlinear electromechanical dynamics are analyzed through Lyapunov exponents, bifurcation diagrams, phase portraits, recurrence plots, Poincar\'e sections, and Fourier spectra. The results reveal parameter-dependent transitions between quasi-periodic and chaotic oscillations, as well as signatures of coexisting dynamical regimes. A systematic investigation of the intrinsic memristor parameters, namely the ON-state resistance Ron, the OFF-state resistance Roff, the oxide thickness D, and the ionic mobility \mu_v, demonstrates that memristive effects strongly influence oscillation amplitudes, resonance frequencies, and nonlinear transitions within the coupled thermo-electro-mechanical system. The state-dependent memristance dynamically modulates the electromechanical coupling and redistributes energy between the electrical and mechanical resonators, thereby generating complex oscillatory responses. In addition, the influence of temperature-sensitive memristive parameters is qualitatively examined through variations of the ionic mobility and resistive states. The results indicate that thermal variations can modify both oscillation amplitudes and dynamical regimes, potentially inducing transitions between quasi-periodic and chaotic behaviors. A comparative discussion with Josephson-junction-based MEMS architectures highlights the operational flexibility and room-temperature compatibility of the HP memristor model for thermo-electro-mechanical applications. These findings suggest promising prospects for adaptive nonlinear oscillators, thermo-sensitive sensors, and chaos-driven electromechanical systems.

Explore related subjects

nlin.CD

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

N. G. Koudafokê, Thierry Njougouo, Hilda A. Cerdeira, C. H. Miwadinou. 2026-05-28. Characterization of Chaotic and Homogeneous coexisting dynamics of a Memristive Thermo-Controlled MEMS. https://arxiv.org/abs/2605.29614

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

KEEP EXPLORING

Related papers

arXiv2026

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↗
arXiv2026

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↗
arXiv2026

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↗