| Handbook of porous media (K. Vafai ed) P Tahmasebi, M Sahimi Crc Press, 2015 | 3158* | 2015 |
| A hybrid neural networks-fuzzy logic-genetic algorithm P Tahmasebi, A Hezarkhani Computers & geosciences 42, 18-27, 2012 | 345 | 2012 |
| Multiple-point statistical modeling based on the cross-correlation functions P Tahmasebi, A Hezarkhani, M Sahimi Computational Geosciences 16 (3), 779-797, 2012 | 343 | 2012 |
| Segmentation of digital images using deep convolutional autoencoder networks S Karimpouli, P Tahmasebi Computers & geosciences 126, 142-150, 2019 | 298 | 2019 |
| Machine learning in geo-and environmental sciences: From small to large scale P Tahmasebi, S Kamrava, T Bai, M Sahimi Advances in Water Resources 142, 103619, 2020 | 248 | 2020 |
| A comprehensive study on geometric, topological and fractal characterizations of pore systems in low-permeability reservoirs based on SEM, MICP, NMR, and X-ray CT Y Wu, P Tahmasebi, C Lin, MA Zahid, C Dong, AN Golab, L Ren Marine and Petroleum Geology 103, 12-28, 2019 | 219 | 2019 |
| Reconstruction of three-dimensional porous media using a single thin section P Tahmasebi, M Sahimi Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 85 (6 …, 2012 | 182 | 2012 |
| Cross-Correlation Function for Accurate Reconstruction of Heterogeneous Media P Tahmasebi, M Sahimi Physical Review Letters 110 (7), 078002, 2013 | 178 | 2013 |
| A comparative machine learning study for time series forecasting: ARIMA, LSTM, and Prophet Y Ning, H Kazemi, P Tahmasebi Computers & Geosciences 164, 105126, 2022 | 159 | 2022 |
| Linking morphology of porous media to their macroscopic permeability by deep learning S Kamrava, P Tahmasebi, M Sahimi Transport in Porous Media 131 (2), 427-448, 2020 | 159 | 2020 |
| Applications of adaptive neuro-fuzzy inference system P Tahmasebi, A Hezarkhani Australian Journal of Basic and Applied Sciences 4 (3), 408-420, 2010 | 159 | 2010 |
| Three-Dimensional Stochastic Characterization of SEM Images P Tahmasebi, F Javadpour, M Sahimi Transport in Porous Media 110 (3), 521-531, 2015 | 149 | 2015 |
| Physics informed machine learning: Seismic wave equation S Karimpouli, P Tahmasebi Geoscience Frontiers 11 (6), 1993-2001, 2020 | 139 | 2020 |
| Simulation of Earth textures by conditional image quilting K Mahmud, G Mariethoz, J Caers, P Tahmasebi, A Baker Water Resources Research 50 (4), 3088-3107, 2014 | 136 | 2014 |
| MS-CCSIM: accelerating pattern-based geostatistical simulation of categorical variables using a multi-scale search in Fourier space P Tahmasebi, M Sahimi, J Caers Computers & Geosciences 67, 75-88, 2014 | 132 | 2014 |
| Multiple point statistics: a review P Tahmasebi Handbook of mathematical geosciences: Fifty years of IAMG, 613-643, 2018 | 123 | 2018 |
| Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of PVT properties J Asadisaghandi, P Tahmasebi Journal of Petroleum Science and Engineering 78 (2), 464-475, 2011 | 119 | 2011 |
| Multiscale study for stochastic characterization P Tahmasebi, F Javadpour, M Sahimi, M Piri Advances in Water Resources 89, 91-103, 2016 | 104 | 2016 |
| Comparing training-image based algorithms using an analysis of distance X Tan, P Tahmasebi, J Caers Mathematical Geosciences 46, 149-169, 2014 | 104 | 2014 |
| Image-based velocity estimation of rock using convolutional neural networks S Karimpouli, P Tahmasebi Neural Networks 111, 89-97, 2019 | 102 | 2019 |
