CMES - Computer Modeling in Engineering and Sciences

Aims & Scope

This journal publishes original research papers of reasonable permanent value, in the areas of computational mechanics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua.

Various length scales (quantum, nano, micro, meso, and macro), and various time scales ( picoseconds to hours) are of interest.

Papers which deal with multi-physics problems, as well as those which deal with the interfaces of mechanics, chemistry, and biology, are particularly encouraged.

New computational approaches, and more efficient algorithms, which eventually make near-real-time computations possible, are welcome.

Original papers dealing with new methods such as meshless methods, and mesh-reduction methods are sought.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.5
2024	2.20

SJR (SCImago Journal Rank)

Year	Value
2024	0.450

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	70

Journal Rank

Year	Value
2024	12630

Journal Citation Indicator

Year	Value
2024	2756

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Structural Design Optimization Using Isogeometric Analysis: A Comprehensive Review

  Citation: 46

  Authors: Yingjun, Zhenpei, Zhaohui, Leong

• A Survey of Image Information Hiding Algorithms Based on Deep Learning

  Citation: 23

  Authors: Ruohan, Qi, Chengsheng

• A Deep Learning-Based Computational Algorithm for Identifying Damage Load Condition: An Artificial Intelligence Inverse Problem Solution for Failure Analysis

  Citation: 14

  Authors: Shaofei, Guorong, Tiange, Qijun, Shaofan

• Data-Driven Prediction of Mechanical Properties in Support of Rapid Certification of Additively Manufactured Alloys

  Citation: 13

  Authors: Fuyao, Yu-Chin, Abhinav, Jiten, Gregory B., Wei

• A Non-Ordinary State-Based Peridynamic Formulation for Failure of Concrete Subjected to Impacting Loads

  Citation: 11

  Authors: Liwei, Dan, Yepeng, Lei

• Machine Learning Models of Plastic Flow Based on Representation Theory

  Citation: 10

  Authors: R. E., J. A., C. M., J. T.

• Improved State-Based Peridynamic Lattice Model Including Elasticity, Plasticity and Damage

  Citation: 8

  Authors: Siavash, Walter

• Dynamic Fracture Analysis for Shale Material by Peridynamic Modelling

  Citation: 8

  Authors: Zhanqi, Zhenyu, Zhongtao

• Finite Element Modeling in Drilling of Nimonic C-263 Alloy Using Deform-3D

  Citation: 6

  Authors: M., A. John Presin, C., Rishab

• Computational Fluid Dynamics Analysis of Shroud Design on Hemodynamic Performance and Blood Damage in a Centrifugal Blood Pump

  Citation: 6

  Authors: Guangliang, Yu, Mingrui

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