Applied Mathematics (APM)

Section Information

Applied Mathematics focuses on the development and use of mathematical methods to solve practical problems in science, engineering, technology, economics, and other fields. It bridges theoretical mathematics with real-world applications, providing models, analytical tools, and computational techniques.

Modern research in applied mathematics includes differential equations, numerical analysis, optimization, mathematical modeling, data science, stochastic processes, and computational methods. Advances in algorithms, simulation, machine learning, and high-performance computing continue to expand the scope and impact of applied mathematics.

This section publishes research articles, theoretical and computational studies, reviews, and case reports addressing mathematical modeling, analytical approaches, algorithm development, simulation techniques, and interdisciplinary applications of mathematical tools.

Scope

Differential Equations and Dynamical Systems
- Ordinary and partial differential equations
- Nonlinear dynamics, stability, and chaos theory
- Mathematical modeling of physical, biological, and engineering systems
- Analytical and numerical solution methods
Numerical Analysis and Scientific Computing
- Numerical algorithms for solving equations and systems
- Approximation theory, interpolation, and discretization techniques
- High-performance and parallel computing
- Error analysis, convergence, and computational accuracy
Optimization and Operations Research
- Linear, nonlinear, convex, and combinatorial optimization
- Stochastic, dynamic, and multi-objective optimization
- Decision science, scheduling, and logistics modeling
- Applications in engineering, economics, and system design
Probability, Statistics, and Stochastic Processes
- Random processes, Markov chains, and stochastic modeling
- Statistical inference, regression, and data analysis
- Uncertainty quantification and risk modeling
- Applications in finance, epidemiology, and natural sciences
Computational and Applied Linear Algebra
- Matrix analysis, eigenvalue problems, and factorization
- Numerical linear algebra algorithms
- Linear systems in engineering and scientific computation
- Applications in machine learning and optimization
Mathematical Modeling and Simulation
- Model development for physical, biological, and social systems
- Deterministic and stochastic modeling frameworks
- Simulation methods and validation techniques
- Interdisciplinary applications and real-world problem solving
Discrete Mathematics and Algorithms
- Graph theory, combinatorics, and network analysis
- Algorithm design, complexity, and optimization
- Cryptography, coding theory, and discrete structures
- Applications in computer science and data analytics
Applied Machine Learning and Data Science
- Mathematical foundations of AI and machine learning
- Statistical learning, pattern recognition, and predictive modeling
- Dimensionality reduction and data-driven methods
- Applications across engineering, health, and industry