Computer Assisted Methods in Engineering and Science (CAMES) is an open access, referred international journal, published quarterly, indexed by Scopus, EBSCO, and DOAJ, providing a scientific exchange forum and an authoritative source of information in the field of computational sciences and related areas of applied engineering. The objective of the journal is to support researchers and practitioners by offering them the means facilitating access to the newest research results reported by leading experts in the field, publication of own contributions, and dissemination of information relevant to the scope of the journal.

Papers published in the journal will fall largely into three main categories:

  • Contributions presenting new research methods of mathematical modeling and computer simulations in engineering and applied sciences, including traditional areas such as solid and structural mechanics, material science, fluid dynamics, acoustics and electromagnetics but going beyond them to account for application relevant issues in physics, chemistry, biology and mathematics, scientific computing, large scale optimization, intelligent systems as well as in multi-scale and multi-physics problems.
  • Articles describing novel applications of computational techniques supporting engineering practice and education in areas like mechanical, aerospace, civil, naval, software, chemical and architectural engineering, materials science as well as demonstrations of their practical use in solving real life problems.
  • State-of-the-art tutorials, providing the readership with a guidance on important research directions as observed in the current world literature on computer assisted methods in engineering and sciences.

The journal will also publish book reviews and information on activities of the European Community on Computational Methods in Applied Sciences (ECCOMAS).

ISSN: 2299-3649 (Print), 2956-5839 (Online)

 This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. All articles are published on Creative Common licence CC BY 4.0.

Journal Metrics

MEiN (2023): 70  |  CiteScore 2023: 1.8 (2022: 1.2)  |  SJR 2023: 0.230 (2022: 0.158)  |  SNIP 2023: 0.663  (2022: 0.363)



Current Issue

Vol 31 No 2 (2024)

[CLOSED]Scientific Computing and Learning Analytics for Smart Healthcare Systems

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