These multidisciplinary articles explore the theory, methods, and applications of dynamical systems and their use to model the time evolution of real systems and their use to model the time evolution of real systems. Using a broad spectrum of analyses and measures, these papers aim to facilitate communication among mathematicians, scientists, and practitioners interested in this field.
Explore the following trending articles in Frontiers in Applied Mathematics and Statistics.
The article discusses limitations of low-dimensional neural mass models in capturing brain dynamics. It introduces a spiking neuron network model with an exact mean-field description, enhancing understanding of brain activity patterns.
This perspective article explores applying recurrence analysis to irregular event time series, overcoming challenges of traditional methods. It discusses adaptation and future prospects, offering insights into diverse scientific fields.
The study explores perturbed nonlinear Schrödinger equation dynamics with the modified (G′G2)-expansion method. Analyzing trigonometric, hyperbolic, and rational solutions, it illuminates electromagnetic wave propagation in nonlinear optical fibers.
The study rethinks collective motion in animal groups, showing polarization can emerge solely from attraction. It compares polarization mechanisms, revealing insights into their impact on collective behavior.
The article reviews image encryption schemes using fractional integral transforms, assessing vulnerabilities and threats. Through taxonomy and analysis, it provides a consolidated view on this evolving encryption technique.
Bocquet explores integrating deep learning and data assimilation in climate science, emphasizing geoscientific dynamics, machine learning, and recent advancements like subgrid-scale parameterization and model error correction.
Researchers explore Rayleigh-Taylor and Richtmyer-Meshkov instabilities under inverse-quadratic power-law accelerations, spanning astrophysical to atomic scales. Using group theory, it unveils insights into their transition behaviors.
The study enhances a mathematical model to investigate the impact of shifting tumor-associated macrophages (TAMs) on cancer invasion. Considering spatial and temporal aspects, it suggests strategies for impeding tumor progression.
EnTLHF-KA merges robust filtering with knowledge assimilation for non-linear systems. Outperforms traditional filters in air quality modeling, offering promising strides in forecasting PM2.5 concentrations amid high uncertainty.
Researchers analyze a blender—a hyperbolic invariant set—within a Hénon-like family in 3D space. By constructing nested sub-boxes, they uncover its properties and parameter ranges, shedding light on its dynamics and geometric characteristics.
Read other impactful articles published in Frontiers in Applied Mathematics and Statistics.