Journal of Applied Mathematics

Scope & Guideline

Empowering discovery in the realm of applied mathematics.

Introduction

Immerse yourself in the scholarly insights of Journal of 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
ISSN1110-757x
PublisherHINDAWI LTD
Support Open AccessYes
CountryUnited States
TypeJournal
Convergefrom 2001 to 2024
AbbreviationJ APPL MATH / J. Appl. Math.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON W1T 5HF, ENGLAND

Aims and Scopes

The Journal of Applied Mathematics focuses on the development and application of mathematical methods to solve real-world problems across various fields. Its core areas encompass numerical analysis, mathematical modeling, and computational techniques, with an emphasis on interdisciplinary applications.
  1. Numerical Analysis and Computational Methods:
    The journal publishes research on numerical techniques for solving mathematical problems, including finite element methods, spectral methods, and adaptive algorithms.
  2. Mathematical Modeling:
    Research that involves creating mathematical representations of real-world phenomena, including physical, biological, and engineering problems, is a significant focus.
  3. Applied Partial Differential Equations:
    The journal covers studies on the existence, uniqueness, and qualitative behavior of solutions to partial differential equations, particularly in applied contexts.
  4. Optimization and Control Theory:
    Papers that explore optimization techniques and control strategies for dynamic systems, often in engineering and economics, are prevalent.
  5. Machine Learning and Data Science Applications:
    The integration of machine learning algorithms with mathematical modeling and numerical simulations is increasingly prominent, reflecting a trend towards data-driven methodologies.
The Journal of Applied Mathematics has seen a dynamic evolution in its thematic focus, with several emerging trends that reflect current research interests and technological advancements. These trends indicate a shift toward innovative approaches and interdisciplinary applications.
  1. Machine Learning and AI Integration:
    There is a significant increase in the application of machine learning techniques to solve mathematical problems, including neural networks and data-driven modeling, highlighting the interdisciplinary nature of modern research.
  2. Complex Systems and Nonlinear Dynamics:
    Research on complex systems, including nonlinear dynamics and chaos theory, is gaining traction as researchers explore intricate interactions within mathematical models.
  3. Mathematical Biology and Biophysics:
    The journal is increasingly publishing papers that apply mathematical methods to biological and physical systems, reflecting a growing interest in modeling biological processes and phenomena.
  4. Numerical Methods for Fractional Differential Equations:
    The rise in interest in fractional calculus and its applications to various fields is evident, with a notable increase in publications on numerical methods for fractional differential equations.
  5. Data-Driven Approaches in Applied Mathematics:
    The trend towards data-driven methodologies, including the use of data assimilation and statistical methods for model validation, indicates a shift towards more empirical and practical applications of mathematics.

Declining or Waning

While the Journal of Applied Mathematics has a broad scope, certain themes appear to be declining in prominence over recent years. This may reflect shifts in research interest or advancements in technology that render some traditional methods less relevant.
  1. Classical Analytical Methods:
    There has been a noticeable decrease in papers focusing on purely analytical approaches to solving mathematical problems, as computational methods gain prominence.
  2. Static Mathematical Models:
    Research involving static models with limited applicability is becoming less common, as the focus shifts toward dynamic, time-dependent problems that better reflect real-world complexities.
  3. Traditional Finite Difference Methods:
    There is a waning interest in classical finite difference methods for solving PDEs, as more sophisticated and flexible methods, such as finite element and spectral methods, take precedence.
  4. Simple Linear Systems:
    The study of simple linear systems appears to be declining, with more complex and nonlinear systems receiving greater attention in recent publications.
  5. Homogeneous Boundary Value Problems:
    Research centered on homogeneous boundary value problems is becoming less frequent, as the journal increasingly features studies on more complex boundary conditions and real-world applications.

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