Computational Methods in Applied Mathematics

Scope & Guideline

Transforming Complex Problems into Mathematical Solutions

Introduction

Welcome to the Computational Methods in Applied Mathematics information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of Computational Methods in Applied Mathematics, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN1609-4840
PublisherWALTER DE GRUYTER GMBH
Support Open AccessNo
CountryGermany
TypeJournal
Convergefrom 2001 to 2024
AbbreviationCOMPUT METH APPL MAT / Comput. Methods Appl. Math.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressGENTHINER STRASSE 13, D-10785 BERLIN, GERMANY

Aims and Scopes

The journal 'Computational Methods in Applied Mathematics' focuses on the development and application of numerical techniques for solving a wide range of mathematical problems, particularly those arising from partial differential equations (PDEs). It serves as a platform for innovative computational methodologies and their applications in various scientific and engineering fields.
  1. Numerical Methods for PDEs:
    The journal emphasizes various numerical techniques for solving partial differential equations, including finite element methods, discontinuous Galerkin methods, and finite difference methods.
  2. Adaptive Algorithms:
    There is a strong focus on adaptive algorithms that enhance the accuracy and efficiency of numerical solutions, particularly in complex geometries and varying parameters.
  3. Error Analysis and Estimation:
    The journal frequently publishes studies on a priori and a posteriori error estimates, providing insights into the reliability of numerical methods.
  4. Multiscale and Multiphysics Problems:
    Research on methods to handle multiscale phenomena and multiphysics problems, which are common in applied mathematics, is a consistent area of focus.
  5. Application of Machine Learning:
    The incorporation of machine learning techniques into numerical methods is a growing area, reflecting the journal's commitment to integrating advanced computational techniques.
  6. Inverse Problems:
    The journal includes studies on inverse problems, focusing on the identification of unknown parameters or functions based on observed data.
The journal has witnessed a rise in interest in several innovative research themes that reflect the evolving landscape of computational methods in applied mathematics. These emerging scopes highlight the journal's responsiveness to new challenges and technological advancements.
  1. Machine Learning Integration:
    Research integrating machine learning with traditional numerical methods is on the rise, indicating a trend towards enhancing computational efficiency and accuracy through data-driven approaches.
  2. Adaptive Finite Element Methods:
    There is an increasing focus on adaptive finite element methods that dynamically adjust mesh density and distribution, optimizing computational resources and improving solution accuracy.
  3. Fractional Differential Equations:
    The study of fractional differential equations is gaining traction, presenting unique challenges and opportunities for novel computational techniques.
  4. Multiscale Modeling:
    Emerging interest in multiscale modeling approaches reflects a growing recognition of the need to address complex systems that exhibit behavior across different scales.
  5. Inverse Problems and Parameter Identification:
    An uptick in research on inverse problems, particularly those involving parameter identification in complex systems, demonstrates the importance of accurate modeling in applied mathematics.
  6. Nonlinear Dynamics and Stability Analysis:
    Research focusing on nonlinear dynamics and stability analysis is trending, particularly in applications relating to physical and engineering systems.

Declining or Waning

While 'Computational Methods in Applied Mathematics' continuously evolves, certain areas of research have seen a decline in publication frequency. This can indicate a shift in focus towards more contemporary methodologies or applications.
  1. Traditional Finite Element Methods:
    There appears to be a waning interest in traditional finite element methods without adaptive features, as newer methodologies that incorporate adaptive and robust techniques gain prominence.
  2. Basic Error Estimation Techniques:
    Basic error estimation techniques that do not incorporate modern adaptive strategies or machine learning components are being published less frequently, as the field moves towards more sophisticated approaches.
  3. Single-Scale Approaches:
    Research focusing solely on single-scale problems is declining, with a stronger emphasis now on multiscale approaches that better capture the complexity of real-world phenomena.
  4. Classical Boundary Element Methods:
    While still relevant, classical boundary element methods are being overshadowed by hybrid methods that combine finite element and boundary element techniques, reflecting a shift towards more integrated approaches.

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