ADVANCES IN COMPUTATIONAL MATHEMATICS

Scope & Guideline

Transforming Complex Challenges into Computational Solutions

Introduction

Delve into the academic richness of ADVANCES IN COMPUTATIONAL MATHEMATICS with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN1019-7168
PublisherSPRINGER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1993 to 2024
AbbreviationADV COMPUT MATH / Adv. Comput. Math.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressONE NEW YORK PLAZA, SUITE 4600 , NEW YORK, NY 10004, UNITED STATES

Aims and Scopes

The journal "ADVANCES IN COMPUTATIONAL MATHEMATICS" focuses on the development and application of computational techniques in mathematical modeling, numerical analysis, and algorithm design. It covers a wide array of topics that bridge theoretical mathematics and practical applications in science and engineering.
  1. Numerical Methods and Algorithms:
    The journal emphasizes the development of new numerical methods and algorithms for solving complex mathematical problems, particularly those arising in partial differential equations (PDEs), fluid dynamics, and optimization.
  2. Mathematical Modeling:
    A core area of focus is on mathematical modeling of real-world phenomena, including applications in physics, engineering, and finance, using computational techniques to derive insights.
  3. Error Analysis and Adaptivity:
    Research on error estimation and adaptive methods is prevalent, highlighting the importance of refining computational models for accuracy and efficiency in numerical simulations.
  4. Machine Learning and Data Science:
    The journal increasingly incorporates themes of machine learning and data-driven methods, exploring their integration into computational mathematics, particularly in relation to PDEs and optimization.
  5. Multiscale and Multiphysics Problems:
    There is a consistent focus on tackling multiphysics and multiscale problems, which require sophisticated computational strategies and innovative numerical techniques.
  6. Matrix and Tensor Computations:
    The journal addresses advanced topics in matrix and tensor computations, including low-rank approximations and tensor decompositions, which are essential for handling high-dimensional data.
The journal is witnessing a shift towards several emerging themes that reflect current trends in computational mathematics and its applications.
  1. Stochastic Methods and Uncertainty Quantification:
    There is an increasing emphasis on stochastic methods and uncertainty quantification, reflecting the need to address variability and uncertainty in computational models.
  2. Machine Learning Integration:
    The integration of machine learning techniques into computational mathematics is a prominent trend, with research focusing on using neural networks and data-driven models for solving PDEs and optimization problems.
  3. Advanced Computational Techniques for PDEs:
    Emerging themes include the development of advanced computational techniques for solving complex PDEs, particularly those with nonlinear characteristics and those arising in multiphysical contexts.
  4. Data-driven Approaches and Surrogates:
    Research on data-driven approaches and surrogate modeling is trending, highlighting the importance of efficient computational techniques that leverage large datasets for modeling and simulation.
  5. High-Dimensional and Complex Systems:
    There is a growing interest in high-dimensional problems and complex system simulations, requiring innovative numerical techniques to handle the computational challenges associated with these systems.

Declining or Waning

While the journal has a strong foundation in various computational mathematics themes, certain areas appear to be declining in prominence based on recent publications.
  1. Classical Numerical Analysis:
    There is a noticeable decline in traditional numerical analysis topics, such as basic finite difference and finite element methods, which are being overshadowed by more innovative computational techniques.
  2. Deterministic Modeling Approaches:
    Deterministic modeling approaches are less frequently featured, with a shift towards probabilistic and stochastic methods that account for uncertainties in models.
  3. Basic Computational Techniques:
    The focus on basic computational techniques, such as standard interpolation methods and simple quadrature rules, is waning as the journal pivots towards more complex and sophisticated methods.
  4. Static Problem Solving:
    Research addressing static problems without considering dynamic aspects is becoming less common, as there is a growing emphasis on dynamic and time-dependent problems.

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