**Guest Editors**
Dr. Yilun Shang
Department of Computer and Information Sciences, Northumbria University, UK
Email: yilun.shang@northumbria.ac.uk
Dr. Modjtaba Ghorbani
School of Mathematics, Shahid Rajaee Teacher Training University, Iran
Email: ghorbani30@gmail.com
We are pleased to invite submissions for a special issue on Complex Systems and Applied Mathematics, aimed at exploring the mathematical foundations and tools used to model and understand complex phenomena. Complex systems, characterized by interactions between numerous components, often exhibit emergent behavior that cannot be discerned solely by examining individual elements. Applied mathematics plays a vital role in unraveling the underlying structures and dynamics of such systems, providing insights into diverse areas such as biological networks, social dynamics, and technological infrastructures.
This special issue seeks to bring together innovative research that applies mathematical techniques to real-world complex systems, focusing on themes such as network dynamics, optimization, and computational approaches. By showcasing a variety of mathematical tools, from differential equations to algebraic structures and probabilistic models, the issue aims to highlight the diversity and interdisciplinary nature of the field. We encourage contributions from various subfields of applied mathematics, including graph theory and its applications, discrete structures, operational research, dynamical systems, and computational algorithms. The deadline for manuscript submissions is 31 March 2025 and all papers will be published before June.
Topics:
We invite original research articles, review articles, and case studies that address, but are not limited to, the following themes:
- Graph theory and its applications
- Dynamical systems and nonlinear analysis
- Network theory and system interactions
- Optimization in networks and graphs
- Computational methods for network systems
- Applications in real-world systems
- Game Theory and Strategic Interactions in Networks
- Machine Learning Approaches for Network Analysis
- Resilience and Robustness in Networked Systems