Interdisciplinary Applications – Mathematics (IAM)
Section Information
Interdisciplinary Applications – Mathematics focuses on the use of mathematical concepts, models, and computational techniques to solve problems across diverse scientific, technological, and social fields. It highlights how mathematics supports discovery, innovation, and decision-making in areas that extend beyond traditional mathematical boundaries.
Modern interdisciplinary research involves applications in biology, physics, engineering, computer science, finance, social sciences, health sciences, environmental studies, and data science. This includes mathematical modeling, optimization, simulation, machine learning, network science, and quantitative analysis tailored to domain-specific challenges.
This section publishes applied studies, modeling work, computational approaches, theoretical developments, reviews, and case analyses that demonstrate how mathematics contributes to understanding complex systems and solving interdisciplinary problems.
Scope
- Mathematics in Physical and Engineering Sciences
- Modeling of mechanical, electrical, and thermal systems
- Wave propagation, material behavior, and fluid dynamics
- Optimization and control in engineering applications
- Simulation and computational tools for physical processes
- Mathematics in Biological and Medical Sciences
- Population dynamics, epidemiological modeling, and disease spread
- Systems biology, biochemical networks, and physiological modeling
- Biomedical imaging, signal analysis, and diagnostic algorithms
- Genomic data analysis, bioinformatics, and machine learning in health
- Environmental and Earth System Modeling
- Climate models, atmospheric dynamics, and hydrological systems
- Ecological modeling and resource management
- Environmental risk assessment and sustainability analytics
- Geospatial modeling and remote sensing data analysis
- Mathematics in Social Sciences and Economics
- Game theory, decision theory, and behavioral modeling
- Economic forecasting, econometrics, and financial mathematics
- Social network analysis and collective behavior
- Optimization in public policy and resource allocation
- Data Science, Machine Learning, and AI
- Statistical learning, pattern recognition, and predictive modeling
- Optimization algorithms and mathematical foundations of AI
- Big data analytics, dimensionality reduction, and clustering
- Interdisciplinary applications in engineering, business, and health
- Computational Modeling and Simulation
- Numerical methods for complex and multiscale systems
- Agent-based models, cellular automata, and stochastic simulation
- High-performance computing and algorithm development
- Verification, validation, and uncertainty quantification
- Operations Research and Decision Sciences
- Logistics, supply chain optimization, and scheduling problems
- Risk assessment, reliability modeling, and systems analysis
- Network optimization, graph-based methods, and transportation modeling
- Applications in industry, healthcare, and public systems
- Mathematics for Emerging Technologies
- Quantum computing, cryptography, and information theory
- Mathematical tools for robotics, automation, and smart systems
- Models for energy systems, renewable resources, and advanced materials
- Interdisciplinary approaches supporting innovation in technology
Editorial Board
Topical Advisory Panel
Papers Published
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