Applied Numerical Mathematics

Scope & Guideline

Exploring New Horizons in Applied Mathematics.

Introduction

Welcome to your portal for understanding Applied Numerical Mathematics, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageEnglish
ISSN0168-9274
PublisherELSEVIER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1985 to 2025
AbbreviationAPPL NUMER MATH / Appl. Numer. Math.
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

Aims and Scopes

Applied Numerical Mathematics focuses on the development and application of advanced numerical methods for solving mathematical problems that arise in various scientific and engineering fields. The journal emphasizes the theoretical underpinnings of numerical techniques as well as their practical implementations, ensuring a comprehensive understanding of numerical analysis.
  1. Numerical Methods for Differential Equations:
    The journal extensively covers numerical methods for both ordinary and partial differential equations, including implicit and explicit schemes, finite element methods, and spectral methods, with a focus on stability and convergence.
  2. Stochastic and Random Processes:
    There is a significant emphasis on the numerical analysis of stochastic differential equations and their applications in fields such as finance, biology, and physics, highlighting techniques for handling randomness and uncertainty.
  3. Fractional Calculus and Integro-Differential Equations:
    The journal features research on fractional calculus, including numerical methods for fractional differential equations and integro-differential equations, reflecting the growing interest in non-integer order derivatives.
  4. Computational Methods for Complex Systems:
    Research articles often address computational techniques for simulating complex physical systems, such as fluid dynamics, heat transfer, and biological processes, utilizing methods like multi-scale modeling and adaptive mesh refinement.
  5. Error Analysis and Algorithm Development:
    The journal emphasizes rigorous error analysis of numerical algorithms, promoting the development of new methods that improve accuracy and efficiency in computational tasks.
  6. Applications in Engineering and Science:
    Applied Numerical Mathematics publishes studies that apply numerical methods to real-world problems in engineering, physics, and other scientific disciplines, demonstrating the practical relevance of theoretical developments.
Recent years have seen a notable evolution in the themes explored by Applied Numerical Mathematics, with several emerging trends reflecting advancements in computational techniques and applications.
  1. Machine Learning and Data-Driven Methods:
    There is an increasing trend towards integrating machine learning techniques into numerical methods, particularly in optimization, data analysis, and solving differential equations.
  2. Multiscale and Multiphysics Problems:
    Research focusing on multiscale and multiphysics problems is gaining traction, driven by the need to model complex interactions in physical systems, as seen in applications like fluid-structure interactions and biological processes.
  3. Adaptive and High-Order Methods:
    Emerging methods that emphasize adaptivity and high-order accuracy are becoming more prevalent, allowing for more efficient computations in complex domains and improving the accuracy of numerical solutions.
  4. Numerical Techniques for Fractional Differential Equations:
    There is a growing interest in numerical methods specifically designed for fractional differential equations, reflecting their increasing importance in modeling real-world phenomena.
  5. Stochastic Modeling and Uncertainty Quantification:
    The application of numerical methods to stochastic models and uncertainty quantification is trending, showcasing a shift towards addressing randomness and variability in mathematical modeling.

Declining or Waning

While Applied Numerical Mathematics continues to explore a broad range of topics, certain areas of focus have shown signs of declining prominence in recent publications. This trend reflects shifts in research interests and advancements in numerical techniques.
  1. Traditional Numerical Linear Algebra:
    The focus on classical topics in numerical linear algebra has waned, as researchers increasingly explore more specialized or advanced techniques, such as tensor methods and machine learning approaches in numerical computations.
  2. Basic Finite Difference Methods:
    There appears to be a decrease in the number of papers dedicated to basic finite difference methods, as the field moves towards more sophisticated approaches such as high-order and adaptive methods.
  3. Low-Dimensional and Simplistic Models:
    Research on low-dimensional models or simplistic approaches to numerical problems has decreased, with a growing emphasis on complex and multi-dimensional systems that require more advanced numerical techniques.

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