Yánez Arcos, Dayanara Lissette
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Yánez Arcos, Dayanara Lissette
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Ambato
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dlyanez@indoamerica.edu.ec
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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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Intelligent Automation of Quantum Transport Simulation: AI-Driven Modeling of Tunneling and Resonance in One-Dimensional Potentials

2026 , Yánez Arcos, Dayanara Lissette

Accurate simulation and analysis of quantum transport phenomena are essential for designing next-generation nanoscale devices in communication and computing. A hybrid computational framework is developed to simulate Gaussian wave packet dynamics using the Crank–Nicolson method, integrated with automationAutomation pipelines and AI-based modeling. Quantum interactions with single barriers, double barriers, and potential wellsPotential wells are explored in detail, revealing transmission behaviors consistent with analytical predictions. Resonance conditions are identified through automated signal processing, and surrogate models trained via machine learningMachine learning enable fast, accurate prediction of transmission coefficients across varying geometries and energies. Numerical results demonstrate excellent agreement with theoretical models, with deviations below 3%, validating the approach. The intelligent system supports inverse design and classification of quantum behaviors, offering practical tools for engineering resonant tunneling diodes, quantum logic filters, and photonic structures. By combining applied numerical physics with AI and automationAutomation, this work exemplifies intelligent solutions for quantum system design © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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Enhancing Simulation Accuracy in Radioactive Decay Models: Comparative Analysis of Euler and RK2 Methods for Interactive Systems

2026 , Yánez Arcos, Dayanara Lissette

The modeling of radioactive decay processes has long been essential in fields such as nuclear physics, embedded systems, and educational simulations. Traditionally, the Euler method has been used to numerically solve the differential equations governing these phenomena. However, Euler’s method exhibits significant limitations in terms of accuracyAccuracy and stability, particularly in coupled systems or stiff mathematical configurations. To address this issue, this study presents a detailed comparison between the Euler method and the second-order Runge-Kutta method (RK2), aiming to evaluate their performance in terms of error accumulation, temporal fidelity, and the ability to preserve system dynamics. The goal is to determine which method is more suitable for simulations that demand high precision and computational efficiency. Numerical simulations were conducted for dual decay, cyclic decay, and thermistor cooling models using normalized time steps (Δt = 0.01 and 0.05). The results were compared against analytical solutions and analyzed through logarithmic error plots. RK2 consistently outperformed the Euler method, reducing absolute error by up to 80%. For instance, in the dual decay model, Euler’s error reached 3.41 units, while RK2 kept it below 0.62. Moreover, RK2 preserved the oscillatory dynamics in cyclic models and accurately predicted thermal thresholds in thermistor simulations. RK2 proves to be significantly more suitable for dynamic simulations where numerical precision and stability are critical. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

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