APPLIED MATHEMATICAL MODELLING

Scope & Guideline

Unleashing the Power of Mathematics for Real-World Applications

Introduction

Immerse yourself in the scholarly insights of APPLIED MATHEMATICAL MODELLING with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN0307-904x
PublisherELSEVIER SCIENCE INC
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 1976 to 2025
AbbreviationAPPL MATH MODEL / Appl. Math. Model.
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressSTE 800, 230 PARK AVE, NEW YORK, NY 10169

Aims and Scopes

The journal 'Applied Mathematical Modelling' focuses on the development and application of mathematical models to solve real-world problems across various disciplines. It encompasses a wide array of methodologies, emphasizing innovative approaches to modeling, analysis, and computational techniques.
  1. Mathematical Modeling Techniques:
    The journal publishes works that utilize mathematical models to describe physical systems, including but not limited to dynamics, fluid mechanics, heat transfer, and structural analysis.
  2. Computational Methods:
    It emphasizes the development and application of computational methods, such as finite element analysis, mesh-free methods, and numerical simulations, to solve complex mathematical models.
  3. Interdisciplinary Applications:
    Research is often interdisciplinary, bridging gaps between mathematics, engineering, physics, biology, and economics, showcasing how mathematical modeling can address challenges across these domains.
  4. Dynamic Systems Analysis:
    The journal features studies that analyze dynamic systems, focusing on stability, control, and optimization of various engineering and biological systems.
  5. Emerging Technologies:
    It explores the application of mathematical models in emerging technologies, such as renewable energy systems, smart materials, and biomedical engineering.
The journal 'Applied Mathematical Modelling' has been evolving, reflecting current trends and emerging themes in mathematical modeling. This section identifies key areas of growing interest within the journal's recent publications.
  1. Machine Learning and Data-Driven Approaches:
    There is a significant increase in the application of machine learning techniques to enhance mathematical modeling, particularly in predictive analytics and optimization problems.
  2. Fractional Calculus and Nonlocal Models:
    Research incorporating fractional calculus and nonlocal models is gaining traction, reflecting a broader interest in capturing complex behaviors in materials and biological systems.
  3. Sustainability and Environmental Modeling:
    Models addressing sustainability issues, such as environmental impact assessments and resource management, are increasingly featured, highlighting the journal's commitment to addressing global challenges.
  4. Multiscale and Multiphysics Modeling:
    Emerging studies focus on multiscale and multiphysics approaches that integrate various physical phenomena, providing comprehensive insights into complex systems.
  5. Real-Time Simulation and Control Strategies:
    There is a notable trend towards real-time simulation and adaptive control strategies, especially in applications involving robotics and intelligent systems, reflecting advancements in computational capabilities.

Declining or Waning

While 'Applied Mathematical Modelling' continues to thrive in several areas, some themes are becoming less prevalent in recent publications. This may indicate shifting interests within the mathematical modeling community or advancements in specific methodologies.
  1. Traditional Analytical Methods:
    There seems to be a decline in the publication of papers focused solely on traditional analytical methods, as researchers increasingly favor computational approaches and numerical simulations.
  2. Basic Epidemiological Models:
    Basic models that do not incorporate complex dynamics or modern data analysis techniques are less frequently published, as the field moves towards more sophisticated models that account for various factors affecting disease spread.
  3. Static Structural Analysis:
    There is a noticeable reduction in works that solely focus on static structural analysis without considering dynamic loading conditions or time-dependent behaviors, reflecting a trend towards more comprehensive modeling.
  4. Single-Domain Studies:
    Research limited to a single domain (e.g., only mechanical systems or only biological models) appears to be waning, with a shift towards more integrated, multi-disciplinary studies.
  5. Simplistic Forecasting Models:
    The journal has seen fewer simplistic forecasting models that do not leverage advanced techniques such as machine learning or Bayesian inference, indicating a move towards more complex methodologies.

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