Communications in Applied Mathematics and Computational Science

Scope & Guideline

Unleashing the potential of computational techniques and methodologies.

Introduction

Welcome to your portal for understanding Communications in Applied Mathematics and Computational Science, 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
ISSN1559-3940
PublisherMATHEMATICAL SCIENCE PUBL
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2006 to 2024
AbbreviationCOMM APP MATH COM SC / Commun. Appl. Math. Comput. Sci.
Frequency-
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressUNIV CALIFORNIA, DEPT MATHEMATICS, BERKELEY, CA 94720-3840

Aims and Scopes

The journal 'Communications in Applied Mathematics and Computational Science' focuses on the intersection of applied mathematics and computational methodologies. It serves as a platform for disseminating innovative research that enhances the understanding and application of mathematical principles in various scientific domains.
  1. Numerical Analysis and Computational Methods:
    The journal emphasizes the development and application of numerical methods for solving complex mathematical problems, including finite-volume methods, time integration techniques, and algorithmic advancements.
  2. Mathematical Modelling and Simulation:
    Research on mathematical modeling in various fields such as fluid dynamics, plasma physics, and radiation transport is a core focus, showcasing how mathematical frameworks can be used to simulate real-world phenomena.
  3. Interdisciplinary Applications:
    The journal covers a range of interdisciplinary applications, bridging gaps between mathematics, physics, engineering, and other scientific disciplines through computational methods.
  4. Innovative Algorithms and Techniques:
    A significant contribution of the journal is the exploration of new algorithms and computational techniques, including machine learning methods like physics-informed neural networks and Gaussian process modeling.
Recent publications in the journal indicate a significant shift towards innovative computational techniques and emerging themes that reflect current scientific challenges and technological advancements. These trends highlight the journal's adaptability and responsiveness to the evolving landscape of applied mathematics and computational science.
  1. Machine Learning Integration:
    There is a burgeoning interest in integrating machine learning techniques into traditional computational frameworks, as evidenced by the use of physics-informed neural networks and Gaussian processes in modeling.
  2. Adaptive and High-Order Methods:
    Recent works emphasize the development of adaptive methods with high-order accuracy, showcasing a trend towards improving computational efficiency and precision in simulations.
  3. Complex Multiphase and Multiscale Systems:
    The journal is increasingly focusing on complex systems, such as multifluid plasma simulations and interactions in cosmological fluid flows, reflecting a growing interest in understanding phenomena that involve multiple scales and phases.
  4. Stochastic and Statistical Approaches:
    There is a rising trend in the use of stochastic and statistical methods, particularly in relation to uncertainty quantification and data assimilation, which are becoming critical in modeling real-world systems.

Declining or Waning

While the journal has consistently published impactful research, certain themes appear to be decreasing in frequency or relevance. This decline may indicate a shift in research priorities or advancements in alternative methodologies.
  1. Traditional PDE Approaches:
    There has been a noticeable decline in the use of traditional partial differential equations (PDE) methods without modern adaptations or enhancements, as newer approaches and computational techniques gain prominence.
  2. Basic Stochastic Methods:
    The focus on basic stochastic methods seems to be waning, possibly due to the rise of more sophisticated stochastic modeling techniques that integrate advanced computational frameworks.
  3. Simplistic Fluid Dynamics Models:
    Research based solely on simplistic models of fluid dynamics is less frequently published, reflecting a trend towards more complex and realistic simulations that consider multiple interacting factors.

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