Mathematics in Engineering

Scope & Guideline

Advancing interdisciplinary innovation through mathematical insights.

Introduction

Welcome to your portal for understanding Mathematics in Engineering, 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
ISSN-
PublisherAMER INST MATHEMATICAL SCIENCES-AIMS
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationMATH ENG-US / Math. Eng.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressPO BOX 2604, SPRINGFIELD, MO 65801-2604, UNITED STATES

Aims and Scopes

The journal 'Mathematics in Engineering' focuses on the application of mathematical theories and methodologies to solve complex engineering problems. The core areas of research include mathematical modeling, numerical analysis, and the development of computational methods across various engineering disciplines.
  1. Mathematical Modeling:
    The journal emphasizes the formulation and analysis of mathematical models that describe physical, biological, and engineering phenomena, often leading to insights into the underlying processes.
  2. Numerical Analysis and Computational Methods:
    A strong focus on the development and implementation of numerical techniques for solving mathematical problems arising in engineering, including finite element methods, spectral methods, and optimization algorithms.
  3. Interdisciplinary Applications:
    The journal promotes research that bridges mathematics with other fields, such as physics, biology, and materials science, highlighting the versatility of mathematical approaches in addressing real-world challenges.
  4. Theoretical Foundations:
    Contributions that enhance the theoretical understanding of mathematical concepts, particularly those that are applicable to engineering problems, are a staple of the journal.
  5. Emerging Technologies and Methods:
    Research on the application of advanced methodologies, including machine learning and artificial intelligence, in engineering contexts is increasingly represented, reflecting the evolving landscape of engineering challenges.
Recent publications in 'Mathematics in Engineering' have highlighted several emerging themes and trends that reflect the evolving landscape of both mathematics and engineering. These trends indicate a growing interest in interdisciplinary approaches and advanced methodologies.
  1. Machine Learning and Data-Driven Approaches:
    There is a significant increase in research that integrates machine learning techniques with traditional mathematical modeling, indicating a trend towards data-driven solutions for complex engineering problems.
  2. Fractional Calculus and Nonlocal Models:
    The rise in studies involving fractional calculus and nonlocal models suggests a growing recognition of their importance in capturing complex behaviors in physical and engineering systems.
  3. Hybrid and Multiscale Modeling:
    Research focusing on hybrid models that combine multiple scales or methodologies is on the rise, reflecting the need to address complex systems that cannot be fully captured by single-scale or single-method approaches.
  4. Stochastic and Uncertainty Quantification:
    There is an increasing emphasis on incorporating stochastic methods and uncertainty quantification into engineering models, responding to the need for more robust solutions in uncertain environments.
  5. Advanced Computational Techniques:
    The journal is witnessing a trend towards innovative computational techniques, such as virtual element methods and adaptive algorithms, which enhance the efficiency and accuracy of numerical solutions.

Declining or Waning

While 'Mathematics in Engineering' continues to grow in certain areas, some themes have shown a decline in publication frequency or relevance. This may indicate a shift in focus or saturation of the topic within the community.
  1. Classical Fluid Dynamics:
    Research centered on traditional fluid dynamics models has been less prevalent in recent issues, suggesting a possible shift towards more complex or hybrid models that incorporate new methodologies.
  2. Static Structural Analysis:
    There seems to be a waning interest in purely static analysis problems without dynamic or time-dependent considerations, as the field trends toward more dynamic and complex interactions.
  3. Basic Numerical Methods:
    Fundamental numerical methods, such as basic finite difference approaches, are appearing less frequently, potentially indicating a shift towards more sophisticated methods that address complex problems.
  4. Traditional Optimization Techniques:
    There is a noticeable decline in papers focused solely on traditional optimization techniques, as the trend moves towards integrating machine learning and data-driven approaches in optimization problems.
  5. Homogeneous Materials Modeling:
    Research involving simplistic models of homogeneous materials is being overshadowed by studies focusing on composite materials and heterogeneous systems, reflecting the current engineering challenges.

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