Aims and scope

Aims

Acta Computational Science and Engineering aims to advance the understanding and application of computational methods and technologies in solving complex engineering and scientific problems. The journal strives to foster collaboration and innovation in computational sciences, bridging theoretical research with real-world applications. It seeks to serve as a leading platform for researchers, engineers, and scientists to share significant advancements, groundbreaking methodologies, and interdisciplinary research that contribute to progress in applied computational sciences.

Scope

The journal covers a broad range of topics within computational science and engineering, focusing on applied research and innovative computational methods. The scope includes but is not limited to:

  • Computational Modeling and Simulation: Techniques for modeling and simulating scientific and engineering systems, including algorithm development, high-performance computing, and large-scale simulations.
  • Data-Driven Applications: Approaches involving machine learning, data mining, and artificial intelligence to analyze, interpret, and optimize complex data in scientific and engineering contexts.
  • Scientific Computing and Numerical Methods: Advances in numerical algorithms, parallel computing, and other methodologies designed to solve large-scale and high-dimensional computational problems.
  • Optimization and Control: Research on computational optimization, control theory, and decision-making processes within engineering and applied sciences.
  • Computational Engineering Applications: Applications of computational techniques in fields such as mechanical engineering, civil engineering, chemical engineering, and materials science.
  • Visualization and Data Analysis: Methods for visualizing, analyzing, and interpreting large datasets generated by computational and experimental studies.

Areas of Interest

The journal welcomes contributions from researchers working in various interdisciplinary fields. Areas of particular interest include:

  • Artificial Intelligence and Machine Learning: Application of AI and ML algorithms for solving engineering problems and optimizing computational processes.
  • Computational Fluid Dynamics: Studies related to fluid mechanics and the numerical simulation of fluid flow for scientific and industrial applications.
  • Computational Materials Science: Research on the computational modeling and simulation of materials properties and behaviors.
  • Bioinformatics and Computational Biology: Development of computational tools and algorithms for analyzing biological data and modeling biological systems.
  • Quantum Computing: Theoretical and applied research in quantum computing and its applications in solving complex computational problems.
  • Environmental and Climate Modeling: Computational approaches to understanding environmental processes, climate change, and sustainability.
  • Cybersecurity and Cryptography: Computational methods in secure data transmission, encryption, and protecting information systems.
  • Big Data Analytics: Techniques and tools for handling, analyzing, and interpreting large datasets in engineering and applied sciences.
  • Network Modeling and Analysis: Computational analysis of network structures, including communication networks, social networks, and complex systems.
  • Robotics and Automation: Research on computational methods for robotics, automation, and control systems.
  • Computational Neuroscience: Studies on brain modeling, neural networks, and AI inspired by biological processes.
  • Smart Cities and IoT: Computational solutions for urban planning, smart infrastructure, and internet-of-things technology.
  • Digital Signal and Image Processing: Techniques for processing and analyzing digital signals and images in various engineering fields.
  • Computational Finance and Economics: Models for financial data analysis, economic forecasting, and risk assessment.
  • Health Informatics and Biomedical Engineering: Using computational approaches to improve healthcare systems, medical diagnostics, and patient data analysis.
  • Computational Social Science: Applying computational models to understand and predict social behavior, demographic changes, and human interactions.
  • Renewable Energy and Energy Systems: Computational models for optimizing renewable energy resources, grid management, and energy efficiency.
  • Structural and Civil Engineering Simulations: Computational techniques in structural integrity, earthquake simulation, and materials engineering for sustainable infrastructure.
  • Supply Chain and Logistics Optimization: Computational methods in logistics, transportation, and supply chain management for efficiency and cost reduction.