International Journal of Modeling Simulation and Scientific Computing

Scope & Guideline

Empowering Research through Modeling and Simulation Excellence

Introduction

Delve into the academic richness of International Journal of Modeling Simulation and Scientific Computing 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
ISSN1793-9623
PublisherWORLD SCIENTIFIC PUBL CO PTE LTD
Support Open AccessNo
CountrySingapore
TypeJournal
Convergefrom 2010 to 2024
AbbreviationINT J MODEL SIMUL SC / Int. J. Model. Simul. Sci. Comput.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE

Aims and Scopes

The International Journal of Modeling Simulation and Scientific Computing primarily focuses on the development and application of modeling and simulation techniques across various scientific and engineering disciplines. It aims to foster innovative methodologies and computational strategies that enhance the understanding and prediction of complex systems.
  1. Modeling and Simulation Techniques:
    The journal emphasizes novel modeling approaches, including mathematical modeling, agent-based modeling, and computational simulations that address real-world problems across different domains.
  2. Interdisciplinary Applications:
    Research published in the journal spans multiple fields such as engineering, healthcare, environmental science, and information technology, showcasing the versatility of modeling and simulation in addressing diverse challenges.
  3. Emerging Technologies:
    The journal highlights the integration of advanced technologies such as artificial intelligence, machine learning, and IoT into modeling and simulation frameworks, promoting innovative solutions and methodologies.
  4. Data-Driven Approaches:
    There is a significant focus on data-driven modeling techniques that leverage large datasets for predictive analytics, including the use of deep learning and statistical methods.
  5. Collaborative and Distributed Systems:
    Research on the modeling of collaborative systems, including cloud computing and edge computing, is a core area, reflecting the growing importance of these technologies in modern applications.
The journal has witnessed a rise in various trending and emerging themes that reflect the current research landscape and technological advancements. These themes indicate a clear direction in the focus of recent publications.
  1. Artificial Intelligence and Machine Learning:
    There is a significant increase in research integrating AI and machine learning techniques into modeling and simulation frameworks, particularly for predictive analytics and data-driven decision-making.
  2. Digital Twin Technology:
    The concept of digital twins is emerging as a vital area of research, with applications in real-time monitoring, control, and optimization of complex systems across various industries.
  3. Epidemiological Modeling:
    Given the recent global health challenges, there is a noticeable trend in the development of sophisticated models for disease spread, particularly relating to COVID-19 and other infectious diseases.
  4. Cyber-Physical Systems:
    Research focusing on cyber-physical systems, particularly in the context of IoT and smart environments, is gaining momentum as industries seek to enhance connectivity and automation.
  5. Sustainability and Environmental Modeling:
    There is a growing emphasis on modeling approaches that address sustainability and environmental concerns, indicating a shift towards research that supports ecological and resource management.

Declining or Waning

While the journal has consistently published on a wide array of topics, certain themes appear to be declining in frequency or prominence. These waning areas suggest a shift in research focus toward more contemporary issues and methodologies.
  1. Traditional Statistical Methods:
    There has been a noticeable decrease in the publication of papers focusing on classical statistical modeling techniques, as newer, more complex methodologies such as machine learning and AI have gained traction.
  2. Basic Mathematical Models:
    Papers centered solely on fundamental mathematical models, without integration of computational techniques or real-world applications, are becoming less common, indicating a shift towards more applied and complex modeling.
  3. Single-Domain Studies:
    Research that focuses narrowly on a single domain without interdisciplinary integration is less frequently published, as the trend moves towards studies that incorporate multiple fields and collaborative approaches.
  4. Static Models:
    There is a declining interest in static modeling approaches as dynamic and adaptive models that can capture changing conditions and interactions are increasingly favored.
  5. Simplistic Simulation Techniques:
    The journal is moving away from simplistic simulation methodologies that do not incorporate advanced computational techniques or real-world complexities, reflecting a demand for more sophisticated simulations.

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