World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
52
Citations
9781
World Ranking
3641
National Ranking
196

Earth Science

D-Index
47
Citations
8893
World Ranking
3985
National Ranking
209

Overview

Peyman Mostaghimi is affiliated with the University of New South Wales in Australia. Their research focuses predominantly within the field of engineering, with particular attention to ocean engineering, mechanics of materials, mechanical engineering, environmental engineering, and geophysics.

Their main areas of investigation include enhanced oil recovery techniques, hydrocarbon exploration and reservoir analysis, hydraulic fracturing and reservoir analysis, coal properties and utilization, seismic imaging and inversion techniques, groundwater flow and contamination studies, and mineral processing and grinding.

Mostaghimi has published frequently in several academic venues. The most common publication outlets are arXiv (Cornell University) with 12 publications, Fuel with 7, Water Resources Research with 7, Transport in Porous Media with 7, and SSRN Electronic Journal with 6 publications.

Their recent publications include:

  • Deep learning in pore scale imaging and modeling, 2021, Earth-Science Reviews
  • In-situ hydrogen wettability characterisation for underground hydrogen storage, 2022, International Journal of Hydrogen Energy
  • Automated lithology classification from drill core images using convolutional neural networks, 2020, Journal of Petroleum Science and Engineering
  • Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning, 2023, Nature Communications
  • Liquid metal synthesis solvents for metallic crystals, 2022, Science

Frequent collaborators in their work include Ryan T. Armstrong, Ying Da Wang, Kunning Tang, James E. McClure, and Yufu Niu. Collaboration counts range, with Ryan T. Armstrong appearing as a coauthor on 98 publications and Ying Da Wang on 42 publications.

Best Publications

  • Pore-scale imaging and modelling

    Martin J. Blunt;Branko Bijeljic;Hu Dong;Oussama Gharbi

  • Computations of Absolute Permeability on Micro-CT Images

    Peyman Mostaghimi;Martin J. Blunt;Branko Bijeljic

  • Predictions of non-Fickian solute transport in different classes of porous media using direct simulation on pore-scale images

    Branko Bijeljic;Ali Raeini;Peyman Mostaghimi;Martin J. Blunt

  • Signature of non-Fickian solute transport in complex heterogeneous porous media.

    Branko Bijeljic;Peyman Mostaghimi;Martin J. Blunt

  • Porosity and permeability characterization of coal: A micro-computed tomography study

    Hamed Lamei Ramandi;Peyman Mostaghimi;Ryan T. Armstrong;Mohammad Saadatfar

  • Deep learning in pore scale imaging and modeling

    Ying Da Wang;Martin J. Blunt;Ryan T. Armstrong;Peyman Mostaghimi

  • In-situ hydrogen wettability characterisation for underground hydrogen storage

    Unknown

  • Insights into non-Fickian solute transport in carbonates.

    Branko Bijeljic;Peyman Mostaghimi;Martin J. Blunt

  • Simulation of Flow and Dispersion on Pore-Space Images

    Peyman Mostaghimi;Branko Bijeljic;Martin J. Blunt

  • Liquid metal synthesis solvents for metallic crystals

    Unknown

  • Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning

    Unknown

  • Cleat-scale characterisation of coal: An overview

    Peyman Mostaghimi;Ryan T. Armstrong;Alireza Gerami;Yibing Hu

  • Automated lithology classification from drill core images using convolutional neural networks

    Fatimah Alzubaidi;Peyman Mostaghimi;Pawel Swietojanski;Stuart R. Clark

  • Machine learning for predicting properties of porous media from 2d X-ray images

    Naif Alqahtani;Fatimah Alzubaidi;Ryan T. Armstrong;Pawel Swietojanski

  • Coal cleat reconstruction using micro-computed tomography imaging

    Yu Jing;Ryan T. Armstrong;Hamed Lamei Ramandi;Peyman Mostaghimi

  • Enhancing Resolution of Digital Rock Images with Super Resolution Convolutional Neural Networks

    Ying Da Wang;Ryan T. Armstrong;Peyman Mostaghimi

  • Deep neural networks for improving physical accuracy of 2D and 3D multi-mineral segmentation of rock micro-CT images

    Ying Da Wang;Mehdi Shabaninejad;Ryan T. Armstrong;Peyman Mostaghimi

  • Rough-walled discrete fracture network modelling for coal characterisation

    Yu Jing;Ryan T. Armstrong;Peyman Mostaghimi

  • Reservoir Modeling for Flow Simulation by Use of Surfaces, Adaptive Unstructured Meshes, and an Overlapping-Control-Volume Finite-Element Method

    Matthew D. Jackson;James R. Percival;Peyman Mostaghimi;Brendan Tollit

  • Numerical Simulation of Reactive Transport on Micro-CT Images

    Peyman Mostaghimi;Min Liu;Christoph H. Arns

  • Impact of mineralogical heterogeneity on reactive transport modelling

    Min Liu;Mehdi Shabaninejad;Peyman Mostaghimi

  • Digital rock analysis for accurate prediction of fractured media permeability

    Hamed Lamei Ramandi;Peyman Mostaghimi;Ryan T. Armstrong

  • High-resolution pore-scale simulation of dissolution in porous media

    Min Liu;Peyman Mostaghimi

Frequent Co-Authors

Ryan T. Armstrong
Ryan T. Armstrong University of New South Wales
Martin J. Blunt
Martin J. Blunt Imperial College London
Christopher C. Pain
Christopher C. Pain Imperial College London
Steffen Berg
Steffen Berg Shell (Netherlands)
Branko Bijeljic
Branko Bijeljic Imperial College London
Matthew D. Jackson
Matthew D. Jackson Imperial College London
Klaus Regenauer-Lieb
Klaus Regenauer-Lieb University of New South Wales
Majid Ebrahimi Warkiani
Majid Ebrahimi Warkiani University of Technology Sydney
Stefan Iglauer
Stefan Iglauer Edith Cowan University
Christoph H. Arns
Christoph H. Arns University of New South Wales

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