Mathematics, Volume 13, Issue 9

In high-dimensional data analysis, main effects and interaction effects often coexist, especially when complex nonlinear relationships are present. Effective variable selection is crucial for avoiding the curse of dimensionality and enhancing the pre...

We conducted a comprehensive comparative study of numerical solvers for the generalized Korteweg–de Vries (gKdV) equation, focusing on classical Fourier-based Crank–Nicolson methods and physics-informed neural networks (PINNs). Our work b...

This paper introduces a robust formation control strategy for Unmanned Aerial Vehicles (UAVs) designed to maintain coordinated trajectories and relative positioning in three-dimensional space. The proposed methodology addresses the challenges of para...

Few-shot image classification aims to classify unlabeled samples when only a small number of labeled samples are available for each class. Recently, local feature-based few-shot learning methods have made significant progress. However, existing metho...

Dynamic post-buckling behavior of microscale cylindrical shells reinforced with functionally graded carbon nanotubes (FG-CNTs) and conveying microfluid is discussed for the first time. The microshell is embedded in a Kerr foundation and subjected to...

The application of digital inclusive finance in various industries, particularly in rural areas, is gaining significant attention. The traditional agricultural sector, which focuses on rural labor economics (RLE), is more sensitive to financial innov...

Multi-view clustering demonstrates strong performance in various real-world applications. However, real-world data often contain incomplete and uncoupled views. Missing views can lead to the loss of latent information, and uncoupled views create obst...

This study proposes a strategy for decomposing the computational domain to solve differential equations using physics-informed neural networks (PINNs) and progressively saving the trained model in each subdomain. The proposed progressive domain decom...

This study constructed a DGC-t-MSV model by integrating dynamic correlation and Granger causality into the MSV framework. Using daily closing price data from 4 January 2022 to 21 November 2024, it empirically analyzed volatility spillover effects bet...

Modeling complex, non-stationary dynamics remains challenging for deterministic neural networks. We present the Chaos-Integrated Synaptic-Memory Network (CISMN), which embeds controlled chaos across four modules—Chaotic Memory Cells, Chaotic Pl...