CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES

Scope & Guideline

Pioneering Insights in Software Engineering and Modeling

Introduction

Welcome to your portal for understanding CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 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
ISSN1526-1492
PublisherTECH SCIENCE PRESS
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2000 to 2024
AbbreviationCMES-COMP MODEL ENG / CMES-Comp. Model. Eng. Sci.
Frequency12 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address871 CORONADO CENTER DR, SUTE 200, HENDERSON, NV 89052

Aims and Scopes

The journal CMES - Computer Modeling in Engineering and Sciences focuses on advancing the field of computational modeling across various engineering disciplines and sciences. It aims to publish high-quality research that contributes to the development of new methodologies, algorithms, and applications in this area.
  1. Computational Modeling Techniques:
    The journal emphasizes the development and application of computational modeling techniques, including finite element methods, boundary element methods, and meshless methods. These techniques are crucial for solving complex engineering problems and simulating physical phenomena.
  2. Interdisciplinary Applications:
    Research published in the journal spans multiple disciplines, including mechanical engineering, civil engineering, biomedical engineering, and materials science. This interdisciplinary approach enhances the applicability of computational models to real-world problems.
  3. Optimization and Design:
    A significant focus is placed on optimization techniques, including topology optimization and design optimization. These studies aim to improve the performance and efficiency of engineering systems through innovative design methodologies.
  4. Machine Learning and AI Integration:
    The integration of machine learning and artificial intelligence into computational modeling is a growing area of interest. The journal encourages submissions that explore how AI can enhance modeling accuracy and efficiency.
  5. Numerical Simulation and Analysis:
    Numerical simulations play a pivotal role in validating theoretical models. The journal publishes research that employs numerical analysis to explore and validate complex systems across various engineering domains.
  6. Sustainability and Environmental Impact:
    With rising global concerns about sustainability, the journal addresses the modeling of environmental impacts and sustainable engineering practices, promoting research that contributes to ecological balance and resource efficiency.
The journal CMES is witnessing a rise in interest in several emerging themes that reflect current technological advancements and societal needs. These trends indicate a proactive approach to addressing contemporary challenges in engineering and sciences.
  1. Advanced Machine Learning Applications:
    There is a noticeable increase in the application of advanced machine learning techniques within computational modeling. This includes the use of deep learning for image recognition, predictive analytics, and optimization problems, signifying a trend towards data-driven methodologies.
  2. Cybersecurity and Resilience in Engineering:
    Research focusing on cybersecurity measures within engineering systems, particularly in IoT and smart grid applications, is gaining traction. This reflects a broader societal concern regarding the security of interconnected systems and the need for resilience against cyber threats.
  3. Sustainable and Green Engineering Practices:
    An emerging theme is the modeling of sustainable engineering practices, including renewable energy systems and environmental impact assessments. This trend aligns with global sustainability goals and highlights the engineering community's responsibility towards ecological preservation.
  4. Integration of Digital Twin Technology:
    The use of digital twin technology for real-time monitoring and predictive maintenance in engineering systems is increasingly prevalent. This integration signifies a shift towards more dynamic and responsive engineering solutions.
  5. Interdisciplinary Research Focus:
    There is a growing trend towards interdisciplinary research that combines insights from various fields, such as biology, medicine, and environmental science, with engineering principles. This approach aims to address complex challenges that require multifaceted solutions.

Declining or Waning

While the journal continues to thrive in many areas, certain themes appear to be losing prominence over recent years. This decline may reflect shifting research priorities or the maturation of specific fields.
  1. Traditional Structural Analysis:
    Research focusing solely on traditional structural analysis methods appears to be waning. As computational techniques evolve, there is a noticeable shift towards more advanced modeling approaches that incorporate dynamic and complex interactions.
  2. Standalone Numerical Methods without AI Integration:
    There is a decline in studies that apply numerical methods in isolation, without integrating machine learning or AI. The trend indicates a preference for hybrid approaches that enhance predictive capabilities and model adaptability.
  3. Purely Theoretical Studies:
    The journal is seeing fewer purely theoretical papers that do not extend into practical applications or computational implementations. The focus is increasingly on research that synthesizes theory with practical applications and real-world implications.
  4. Classical Fluid Dynamics Studies:
    Research centered exclusively on classical fluid dynamics, without the integration of modern computational techniques or interdisciplinary applications, is less frequently represented, indicating a shift towards more complex fluid modeling scenarios.

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