Computational Engineering and Physical Modeling

ISO Abbreviation:

Comput. Eng. Phys. Model.


Journal Metrics

Acceptance Rate: 47%

 Review Speed: 141 days


Publication Start Year: 2018

No. of Citations (Scopus): 39

Scopus h-index: 3

No. of Citations (Google Scholar): 94

Google Scholar h-index: 5

Issue Per Year: 4

No. of Volumes: 4

No. of Issues: 16

No. of Articles: 92

No. of Indexing Databases: 9

No. of Reviewers: 1081

No. of Contributors: 215

Contributing Countries: 13


Article View: 62,498

PDF Download: 49,982

View Per Article: 679.33

PDF Download Per Article: 543.28




About Journal


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The Journal of Computational Engineering and Physical Modeling (CEPM) is an international open-access journal (online) published quarterly by Pouyan Press which was founded in 2017. Recent developments of experimental studies along with computational techniques lead to the comprehensive understanding of engineering phenomena. Journal of Computational Engineering and Physical Modeling (CEPM) is offering the multidisciplinary forum for the latest researches and advanced practices to be published in the area of computational engineering and physical modeling.

Aims and Scope

Investigations and reviews in all features of experimental studies and physical modeling at any scale and numerical methods in entire engineering backgrounds such as aerospace, chemical, civil, mechanical, environmental, architectural, electrical engineering, and computational biology, chemistry, and materials science are included (but not limited) in the subjects.

General scopes can be categorized as follow:

  • Civil Engineering
  • Mechanical Engineering
  • Aerospace Engineering and Technology
  • Chemical Engineering
  • Materials Science
  • Marine Engineering
  • Engineering Geology and Oceanography
  • Mining Engineering
  • Environmental Science and Engineering
  • Architectural Engineering
  • Computational Biology

DOI Prefix: 10.22115

No publication fee! The journal welcomes article submissions and does not charge a publication fee.

Current Issue: Volume 4, Issue 4 - Serial Number 16, Autumn 2021, Pages 1-108 

Original Article

1. The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete

Pages 1-25


Atefehossadat Khademi; Kiachehr Behfarnia; Tanja Kalman Šipoš; Ivana Miličević

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