| A case study on homogeneous and heterogeneous reservoir porous media reconstruction by using generative adversarial networks S Liu, Z Zhong, A Takbiri-Borujeni, M Kazemi, Q Fu, Y Yang Energy Procedia 158, 6164-6169, 2019 | 33 | 2019 |
| Dew point pressure prediction based on mixed-kernels-function support vector machine in gas-condensate reservoir Z Zhong, S Liu, M Kazemi, TR Carr Fuel 232, 600-609, 2018 | 33 | 2018 |
| Molecular simulation of enhanced oil recovery in shale A Takbiri-Borujeni, M Kazemi, S Liu, Z Zhong Energy Procedia 158, 6067-6072, 2019 | 27 | 2019 |
| Application of neural networks in multiphase flow through porous media: Predicting capillary pressure and relative permeability curves S Liu, Z Arsalan, S Shariar, D Amirmasoud, Kalantari, N Shahin Journal of Petroleum Science and Engineering 180, 445-455, 2019 | 25 | 2019 |
| Numerical simulation of water-alternating-gas process for optimizing EOR and carbon storage Z Zhong, S Liu, TR Carr, A Takbiri-Borujeni, M Kazemi, Q Fu Energy Procedia 158, 6079-6086, 2019 | 12 | 2019 |
| PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks S Liu, P Zhang, D Lu, G Zhang International Conference on Learning Representations, 2022 | 11 | 2022 |
| A review of lattice-Boltzmann models coupled with geochemical modeling applied for simulation of advanced waterflooding and enhanced oil recovery processes S Liu, C Zhang, RB Ghahfarokhi Energy & Fuels 35 (17), 13535-13549, 2021 | 10 | 2021 |
| An efficient bayesian method for advancing the application of deep learning in earth science D Lu, S Liu, D Ricciuto 2019 International Conference on Data Mining Workshops (ICDMW), 270-278, 2019 | 10 | 2019 |
| An out-of-distribution-aware autoencoder model for reduced chemical kinetics P Zhang, S Liu, D Lu, R Sankaran, G Zhang Discrete and Continuous Dynamical Systems-Series S 15 (4), 2021 | 9 | 2021 |
| A prediction interval method for uncertainty quantification of regression models P Zhang, S Liu, D Lu, G Zhang, R Sankaran Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), 2021 | 7 | 2021 |
| Pore-scale characterization of eagle ford outcrop and reservoir cores with SEM/BSE, EDS, FIB-SEM, and lattice Boltzmann simulation S Cudjoe, S Liu, R Barati, F Hasiuk, R Goldstein, JS Tsau, B Nicoud, ... SPE Annual Technical Conference and Exhibition?, D031S042R005, 2019 | 7 | 2019 |
| Investigation of hydrometeorological influences on reservoir releases using explainable machine learning methods M Fan, L Zhang, S Liu, T Yang, D Lu Frontiers in Water 5, 1112970, 2023 | 6 | 2023 |
| Identifying hydrometeorological factors influencing reservoir releases using machine learning methods M Fan, L Zhang, S Liu, T Yang, D Lu 2022 IEEE International Conference on Data Mining Workshops (ICDMW), 1102-1110, 2022 | 5 | 2022 |
| Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions D Lu, S Liu, SL Painter, NA Griffiths, EM Pierce Authorea Preprints, 2022 | 5 | 2022 |
| Fast estimation of permeability in sandstones by 3D convolutional neural networks S Liu, R Barati, C Zhang SEG International Exposition and Annual Meeting, D033S046R002, 2019 | 5 | 2019 |
| A deep learning-based direct forecasting of CO2 plume migration M Fan, D Lu, S Liu Geoenergy Science and Engineering 221, 211363, 2023 | 4 | 2023 |
| Improving net ecosystem CO2 flux prediction using memory-based interpretable machine learning S Liu, D Lu, D Ricciuto, A Walker 2022 IEEE International Conference on Data Mining Workshops (ICDMW), 1111-1119, 2022 | 4 | 2022 |
| Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions S Liu, D Lu, SL Painter, NA Griffiths, EM Pierce Frontiers in Water 5, 1150126, 2023 | 3 | 2023 |
| An interpretable machine learning model for advancing terrestrial ecosystem predictions D Lu, DM Ricciuto, S Liu Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), 2022 | 3 | 2022 |
| Application of variational policy gradient to atomic-scale materials synthesis S Liu, N Borodinov, L Vlcek, D Lu, N Laanait, RK Vasudevan arXiv preprint arXiv:2006.15644, 2020 | 3 | 2020 |
