STUDIES IN APPLIED MATHEMATICS

Scope & Guideline

Empowering researchers with rigorous applied mathematics.

Introduction

Immerse yourself in the scholarly insights of STUDIES IN APPLIED MATHEMATICS 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
ISSN0022-2526
PublisherWILEY
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 1922 to 1928, from 1930 to 1949, from 1969 to 2024
AbbreviationSTUD APPL MATH / Stud. Appl. Math.
Frequency8 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address111 RIVER ST, HOBOKEN 07030-5774, NJ

Aims and Scopes

The journal 'Studies in Applied Mathematics' focuses on the application of mathematical theories and methodologies to solve real-world problems across various scientific fields. It encompasses a wide range of topics that demonstrate the intersection of mathematics with physics, engineering, biology, and other disciplines.
  1. Applied Mathematical Modeling:
    The journal emphasizes the development and analysis of mathematical models that represent complex phenomena in various fields such as fluid dynamics, epidemiology, and ecological systems.
  2. Advanced Computational Techniques:
    Research often incorporates numerical methods and computational simulations to solve partial differential equations and other mathematical models, highlighting the practical implementation of theoretical concepts.
  3. Interdisciplinary Applications:
    Papers frequently explore applications of mathematics in diverse areas like physics, biology, and engineering, showcasing the versatility of mathematical methods in addressing real-world challenges.
  4. Analysis of Nonlinear Systems:
    The journal includes studies on nonlinear dynamics, bifurcation theory, and stability analysis, which are crucial for understanding complex systems and their behaviors.
  5. Asymptotic Analysis and Perturbation Methods:
    Research often employs asymptotic techniques to derive simplified models or to understand the behavior of solutions in various limits, providing insights into the underlying mathematical structures.
The journal has shown a significant evolution in its published themes, indicating a shift towards more contemporary and relevant research areas that align with current scientific challenges.
  1. Machine Learning and Data-Driven Approaches:
    Recent publications increasingly incorporate machine learning techniques for solving inverse problems and enhancing model predictions, reflecting a trend towards data-driven methodologies in applied mathematics.
  2. Complex Systems and Network Dynamics:
    There is a growing interest in the mathematical modeling of complex systems, particularly in the context of networks, population dynamics, and the spread of diseases, showcasing the relevance of interdisciplinary studies.
  3. Nonlocal and Fractional Derivative Models:
    Emerging research focuses on nonlocal effects and fractional calculus, indicating a trend towards more sophisticated models that capture memory effects and spatial interactions in various systems.
  4. Stochastic Modeling and Uncertainty Quantification:
    An increase in studies addressing stochastic processes and uncertainty quantification highlights the importance of probabilistic approaches in applied mathematics, particularly in fields like epidemiology and environmental science.

Declining or Waning

While the journal continues to thrive in many areas, certain themes have seen a decline in focus over recent years, reflecting shifts in research trends and interests.
  1. Linear Models and Simplistic Approaches:
    There has been a noticeable reduction in the publication of papers focusing solely on linear models and simplistic mathematical approaches, as researchers increasingly seek to address the complexities of real-world systems.
  2. Traditional Analytical Techniques:
    The reliance on classical analytical methods has diminished, with a growing preference for numerical simulations and computational methods that can handle nonlinear problems more effectively.
  3. Static Systems Analysis:
    Research concentrating on static or equilibrium systems has become less prevalent, as dynamic and time-dependent models gain more attention due to their relevance in contemporary scientific inquiries.

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