Yánez Arcos, Dayanara Lissette
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Yánez Arcos, Dayanara Lissette
Main Affiliation
Ambato
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dlyanez@indoamerica.edu.ec
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Scopus Author ID
58719098600

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Dual Regimes of Nonlinear Energy Dynamics in FPUT Lattices: From Recurrence to Chaos in Communication-Oriented Systems

2026 , Yánez Arcos, Dayanara Lissette

Nonlinear oscillator networks such as the Fermi–Pasta–Ulam–Tsingou (FPUT) chain exhibit rich dynamical behavior that bridges integrable motion and chaotic energy diffusion. In this work, a comprehensive numerical and theoretical framework is developed to analyze the transition from coherent modal excitation to ergodicity, with a particular focus on its implications for signal propagation and wave-based computation. A dual decay model is introduced to characterize the critical energy threshold required for ergodic behavior, revealing distinct exponential and power-law scaling regimes as functions of the nonlinear coupling parameter. Numerical simulations further demonstrate that this threshold scales inversely with system size, highlighting the role of mode density in enabling energy delocalization. Spectral analyses reveal both reversible modal recurrences and irreversible energy cascades, while phase-space diagnostics via Poincaré sections uncover the progressive breakdown of invariant structures and the emergence of global chaos. The results provide a predictive framework for tuning energy and nonlinearity to preserve coherence or induce controlled randomness in oscillator-based communication and computing systems. The integration of dynamical systems theory with applied nonlinear modeling offers new tools for the design of robust, tunable, and scalable information technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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Smart Material Phase Classification Using Machine Learning on Ising Lattice Simulations

2026 , Yánez Rueda, Hugo , Yánez Arcos, Dayanara Lissette

Understanding and classifying phase transitionsPhase transitions in lattice-based systems remain central challenges in statistical physics and material science, particularly when conventional order parameters become ambiguous. Recent advances in machine learningMachine learning have shown promise in automating phase detection, yet many approaches either focus narrowly on identifying the critical temperature or rely on raw spin configurations, which can hinder interpretability and robustness. In this work, we develop a hybrid computational framework that integrates Monte Carlo simulations of the two-dimensional Ising modelIsing model with unsupervised machine learningMachine learning techniques to address these challenges. By extracting high-level thermodynamic observables—magnetization, energy, specific heat, and susceptibility—we construct a feature-rich dataset that captures both average behavior and fluctuation-driven response. Dimensionality reduction via Principal Component Analysis (PCA), followed by KMeans clustering, enables data-driven classification of ferromagnetic, paramagnetic, and antiferromagnetic regimes. The framework successfully identifies first-order transitions through hysteresis loops, resolves second-order critical phenomena near Tc ≈ 2.269, and detects the Néel transition in antiferromagnetic systems where traditional magnetization fails. These findings highlight the novelty of using thermodynamic observables for interpretable ML-based phase classification and demonstrate the potential of this approach for analyzing smart materialsSmart materials and adaptive technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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