Journal of Computational Science - Elsevier - Impact Factor

Journal of Computational Science - Elsevier | 2024 Impact Factor:3.7 | Cite Score:6.5 | Q2

Impact Factor and Journal Rank of Journal of Computational Science

About: The Journal of Computational Science serves as an international platform for researchers and practitioners across scientific and engineering disciplines to exchange ideas and advancements in computational methodologies and their applications. The journal aims to foster collaboration and innovation in computational science through the publication of high-quality research articles.
Objective:
The journal aims to publish innovative research results in simulation-based science across all scientific disciplines. It focuses on solving complex, multi-scale, and multi-domain problems in science and engineering, integrating advanced numerical methods, computation, data, networks, and novel devices.
Peer Review Process:
To ensure the quality and relevance of published articles, the journal employs a rigorous peer-review process. Each submission undergoes evaluation by experts in the field, ensuring that only scientifically robust research that contributes significantly to computational science is accepted.
Innovation and Impact:
The journal highlights methodologies, algorithms, and software tools for modeling, simulation, analysis, and visualization across various disciplines. It aims to advance computational science by showcasing cutting-edge research that addresses real-world challenges and drives technological innovation.
Global Reach and Collaboration:
With a global readership and contributors from diverse scientific backgrounds, the journal promotes collaboration and knowledge exchange. It facilitates interdisciplinary research initiatives and encourages the integration of computational techniques into different scientific domains.
Interdisciplinary Research:
Recognizing the interdisciplinary nature of computational science, the journal welcomes contributions that span traditional scientific boundaries. It encourages researchers to explore new methodologies and applications that leverage computational tools to solve complex problems.
Practical Applications and Methodologies:
In addition to theoretical advancements, the journal emphasizes the practical applications of computational science. It covers topics such as advanced modeling techniques, simulation-driven discoveries, data-intensive computations, and visualization methods, providing insights and tools that benefit both researchers and practitioners.

Journal Home: Journal Homepage

Editor-in-Chief: Sarika Jalan

Scope: The Journal of Computational Science (JCS) publishes high-quality research articles that span all areas of computational science. It focuses on methodologies, algorithms, and software tools for modeling, simulation, analysis, and visualization across various disciplines. Key areas of interest include, but are not limited to:
Computational Methods and Algorithms:
Development and analysis of novel algorithms and methods for computational modeling, simulation, optimization, and data analysis.
High-Performance Computing:
Advancements in hardware and software technologies for high-performance computing systems, including parallel and distributed computing.
Computational Biology and Bioinformatics:
Applications of computational techniques in biological and biomedical research, including genomics, proteomics, systems biology, and drug discovery.
Computational Physics and Chemistry:
Modeling and simulation of physical and chemical phenomena using computational approaches, including molecular dynamics, quantum chemistry, and materials science.
Computational Engineering:
Application of computational methods in various engineering disciplines such as aerospace, mechanical, civil, and environmental engineering.
Data Science and Machine Learning:
Innovative applications of data science techniques, including machine learning, deep learning, and data mining, to solve complex computational problems.
Computational Social Sciences and Economics:
Use of computational methods to study social phenomena, economic systems, and complex networks, including agent-based modeling and computational game theory.
Visualization and Virtual Reality:
Techniques and tools for visualizing and interacting with large-scale computational data sets, including immersive virtual reality and augmented reality.
Interdisciplinary Applications:
Cross-disciplinary applications of computational science methodologies to address challenges in fields such as climate modeling, urban planning, finance, and cybersecurity.