| 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 | 245 | 2020 |
| 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 | 158 | 2020 |
| Enhancing images of nanoscale porous materials by a hybrid stochastic and deep learning algorithm S Kamrava, P Tahmasebi, M Sahimi Neural Networks 118, 310-320, 2019 | 99* | 2019 |
| Rapid multiscale modeling of flow in porous media P Tahmasebi, S Kamrava Physical Review E 98 (5), 052901, 2018 | 58 | 2018 |
| Simulating fluid flow in complex porous materials by integrating the governing equations with deep-layered machines S Kamrava, M Sahimi, P Tahmasebi Nature Computational Materials 7 (1), 1-9, 2021 | 44 | 2021 |
| Physics-and image-based prediction of fluid flow and transport in complex porous membranes and materials by deep learning S Kamrava, P Tahmasebi, M Sahimi Journal of Membrane Science 622, 119050, 2021 | 42 | 2021 |
| A pore-scale mathematical modeling of fluid-particle interactions: Thermo-hydro-mechanical coupling P Tahmasebi, S Kamrava International Journal of Greenhouse Gas Control 83, 245-255, 2019 | 41 | 2019 |
| Managing abnormal operation through process integration and cogeneration systems S Kamrava, KJ Gabriel, MM El-Halwagi, FT Eljack Clean Technologies and Environmental Policy 17 (1), 119-128, 2015 | 40 | 2015 |
| Managing abnormal operation through process integration and cogeneration systems S Kamrava Texas A&M University, 2014 | 40 | 2014 |
| Quantifying accuracy of stochastic methods of reconstructing complex materials by deep learning S Kamrava, M Sahimi, P Tahmasebi Physical Review E 101 (4), 043301, 2020 | 23 | 2020 |
| Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network S Kamrava, J Im, FPJ de Barros, M Sahimi Geophysical Research Letters, 2021 | 22 | 2021 |
| Effect of wettability on two-phase flow through granular porous media: fluid rupture and mechanics of the media MA Hosseini, S Kamrava, M Sahimi, P Tahmasebi Chemical Engineering Science 269, 118446, 2023 | 15 | 2023 |
| A multiscale approach for flow consistent modeling P Tahmasebi, S Kamrava Transport in Porous Media 124 (1), 237-261, 2018 | 15 | 2018 |
| Modeling the physical properties of hydrate‐bearing sediments: Considering the effects of occurrence patterns Y Wu, P Tahmasebi, K Liu, C Lin, S Kamrava, S Liu, S Fagbemi, C Liu, ... Energy 278, 127674, 2023 | 14 | 2023 |
| Estimation of internal states in a Li-ion battery using BiLSTM with Bayesian hyperparameter optimization H Mirzaee, S Kamrava Journal of Energy Storage 74, 109522, 2023 | 10 | 2023 |
| An end-to-end approach to predict physical properties of heterogeneous porous media: Coupling deep learning and physics-based features Y Wu, S An, P Tahmasebi, K Liu, C Lin, S Kamrava, C Liu, C Yu, T Zhang, ... Fuel 352, 128753, 2023 | 9 | 2023 |
| End-to-end three-dimensional designing of complex disordered materials from limited data using machine learning S Kamrava, H Mirzaee Physical Review E 106 (5), 055301, 2022 | 9 | 2022 |
| Charge-density based convolutional neural networks for stacking fault energy prediction in concentrated alloys G Arora, S Kamrava, P Tahmasebi, DS Aidhy Materialia 26, 101620, 2022 | 8 | 2022 |
| Minireview on porous media and microstructure reconstruction using machine learning techniques: Recent advances and outlook H Mirzaee, S Kamrava, P Tahmasebi Energy & Fuels 37 (20), 15348-15372, 2023 | 7 | 2023 |
| Simulating fluid flow in complex porous materials by integrating the governing equations with deep-layered machines. npj Computational Materials, 7 (1), 1–9 S Kamrava, M Sahimi, P Tahmasebi | 6 | 2021 |
