Modelling

Scope & Guideline

Unlocking Insights Across Scientific Disciplines.

Introduction

Delve into the academic richness of Modelling 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
ISSN-
PublisherMDPI
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationMODELLING-BASEL / Modelling
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND

Aims and Scopes

The journal 'Modelling' focuses on the development, application, and evaluation of models across various domains of engineering and science. It emphasizes innovative methodologies and the utilization of advanced computational techniques to solve complex problems. The journal serves as a platform for researchers to disseminate their findings in modeling approaches that enhance understanding, prediction, and optimization in diverse fields.
  1. Computational Modeling Techniques:
    The journal showcases a range of computational techniques including finite element analysis, machine learning, and simulation methods that are applied to various engineering problems.
  2. Interdisciplinary Applications:
    Research published in 'Modelling' spans multiple disciplines such as mechanical engineering, civil engineering, environmental science, and healthcare, demonstrating the interdisciplinary nature of modeling.
  3. Optimization and Decision Support:
    Many papers focus on optimization methodologies for improving system performance and decision-making processes, reflecting the journal's commitment to practical applications.
  4. Data-Driven Approaches:
    There is a consistent emphasis on data-driven modeling approaches, particularly the integration of machine learning and statistical methods to enhance model accuracy and reliability.
  5. Real-Time and Predictive Modeling:
    The journal includes studies that develop real-time and predictive models, emphasizing their importance in applications such as smart manufacturing, urban planning, and environmental monitoring.
Recent publications in 'Modelling' reflect emerging themes that are gaining traction. This section highlights these trends, indicating the journal's responsiveness to contemporary challenges and advances in technology.
  1. Machine Learning Integration:
    An increasing number of papers incorporate machine learning techniques into modeling processes, reflecting the growing importance of AI in enhancing predictive capabilities.
  2. Sustainability and Environmental Modeling:
    There is a notable rise in research focused on sustainability, with models addressing environmental impacts, resource management, and sustainable engineering practices.
  3. Real-Time Data Utilization:
    The trend towards real-time data utilization in modeling is on the rise, with applications in smart cities, real-time monitoring systems, and dynamic decision-making.
  4. Complex Systems and Network Modeling:
    Research examining complex systems and network interactions is trending, driven by the need to understand interconnected systems in areas such as urban planning and healthcare.
  5. Digital Twin Technology:
    The concept of digital twins is emerging as a significant area of interest, with applications in manufacturing and urban infrastructure, enabling real-time simulation and optimization.

Declining or Waning

As the field of modeling evolves, certain themes have shown a decline in prevalence within the journal. This section outlines the areas that appear to be waning, reflecting a shift in research focus or changing methodologies.
  1. Traditional Analytical Methods:
    There has been a noticeable decrease in papers relying solely on traditional analytical methods, as more researchers pivot towards computational and data-driven approaches.
  2. Basic Simulation Techniques:
    While foundational simulation techniques were once popular, the recent trend indicates a decline in their application, likely due to the rise of more sophisticated modeling frameworks.
  3. Single-Disciplinary Focus:
    Research that is strictly confined to a single discipline is becoming less common, with a growing preference for interdisciplinary studies that address complex, multifaceted problems.
  4. Static Models:
    The use of static models, which do not account for dynamic changes over time, is decreasing as researchers prioritize dynamic and adaptive models that better represent real-world systems.
  5. Simplistic Optimization Models:
    There is a waning interest in simplistic optimization models that do not incorporate uncertainty or variability, as more complex and robust models gain traction.

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