| Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs A Jiang, Z Qin, D Faulder, TT Cladouhos, B Jafarpour Geothermics 104, 102439, 2022 | 15 | 2022 |
| Physics-guided deep learning for prediction of energy production from geothermal reservoirs Z Qin, A Jiang, D Faulder, TT Cladouhos, B Jafarpour Geothermics 116, 102824, 2024 | 13 | 2024 |
| Efficient optimization of energy recovery from geothermal reservoirs with recurrent neural network predictive models Z Qin, A Jiang, D Faulder, TT Cladouhos, B Jafarpour Water Resources Research 59 (3), e2022WR032653, 2023 | 10 | 2023 |
| A study on selecting optimum flash and evaporation temperatures for four geothermal power generation systems under different geofluid’s conditions Y Zhao, X Lu, J Zhu, W Zhang, K Hu, G Xin, F Jiang, Z Qin Energy Procedia 142, 439-446, 2017 | 9 | 2017 |
| A Multiscale Recurrent Neural Network Model for Long-Term Prediction of Geothermal Energy Production A Jiang, Z Qin, TT Cladouhos, D Faulder, B Jafarpour Stanford Geothermal Workshop, 2022 | 8 | 2022 |
| A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs A Jiang, Z Qin, D Faulder, TT Cladouhos, B Jafarpour Geothermics 110, 102643, 2023 | 6 | 2023 |
| Recurrent Neural Networks for Prediction of Geothermal Reservoir Performance A Jiang, Z Qin, TT Cladouhos, D Faulder, B Jafarpour Stanford Geothermal Workshop, 2021 | 6 | 2021 |
| The lumped-parameter model on two-phase and superheated geothermal reservoir Z Qin, Á Valfells, MS Guðjónsdóttir Energy Procedia 142, 481-487, 2017 | 6 | 2017 |
| Physics-Guided Deep Learning for Prediction of Geothermal Reservoir Performance Z Qin, A Jiang, D Faulder, TT Cladouhos, B Jafarpour Stanford Geothermal Workshop, 2022 | 4* | 2022 |
| Spatio-temporal neural networks for monitoring and prediction of CO2 plume migration from measurable field data Y Liu, Z Qin, F Zheng, B Jafarpour Journal of Cleaner Production 481, 144080, 2024 | 1 | 2024 |
| Physics-Guided Deep Learning for Predicting CO2 Migration Dynamics Z Qin, Y Liu, F Zheng, B Jafarpour ECMOR 2024 2024 (1), 1-17, 2024 | 1 | 2024 |
| Lumped parameter modelling of two-phase geothermal reservoirs Z Qin | 1 | 2018 |
| A Fluid Flow‐Based Deep Learning (FFDL) Architecture for Subsurface Flow Systems With Application to Geologic CO2 Storage Z Qin, Y Liu, F Zheng, B Jafarpour Water Resources Research 61 (1), e2024WR037953, 2025 | | 2025 |
| Spatial-Temporal Neural Networks for Predicting CO2 Migration from Field Measurements Y Liu, Z Qin, F Zheng, B Jafarpour ECMOR 2024 2024 (1), 1-10, 2024 | | 2024 |
| A Deep Learning-Based Geomechanical Surrogate Model for Rapid Risk Assessment in Geologic CO2 Storage F Zheng, B Jha, B Jafarpour, Z Qin ECMOR 2024 2024 (1), 1-13, 2024 | | 2024 |
| Optimization of Energy Production from Geothermal Reservoirs Using Physics-Based and Data-Driven Models Z Qin, A Jiang, D Faulder, TT Cladouhos, B Jafarpour | | |
| Fluid Flow-based Deep Learning (FFDL) for geologic CO2 Storage Z Qin, Y Liu, F Zheng, B Jafarpour | | |
Behnam JafarpourProfessor of Chemical and Electrical Engineering, USCE-mail megerősítve itt: usc.edu