| An adsorbed gas estimation model for shale gas reservoirs via statistical learning Y Chen, S Jiang, D Zhang, C Liu Applied energy 197, 327-341, 2017 | 82 | 2017 |
| Use of multifidelity training data and transfer learning for efficient construction of subsurface flow surrogate models S Jiang, LJ Durlofsky Journal of Computational Physics 474, 111800, 2023 | 53 | 2023 |
| Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR H Tang, P Fu, H Jo, S Jiang, CS Sherman, F Hamon, NA Azzolina, ... International Journal of Greenhouse Gas Control 120, 103765, 2022 | 41 | 2022 |
| A data-space inversion procedure for well control optimization and closed-loop reservoir management S Jiang, W Sun, LJ Durlofsky Computational Geosciences 24 (2), 361-379, 2020 | 38 | 2020 |
| Surrogate model for geological CO2 storage and its use in hierarchical MCMC history matching Y Han, FP Hamon, S Jiang, LJ Durlofsky Advances in Water Resources 187, 104678, 2024 | 33 | 2024 |
| Data-space inversion using a recurrent autoencoder for time-series parameterization S Jiang, LJ Durlofsky Computational Geosciences 25 (1), 411-432, 2021 | 31 | 2021 |
| History matching for geological carbon storage using data-space inversion with spatio-temporal data parameterization S Jiang, LJ Durlofsky International Journal of Greenhouse Gas Control 134, 104124, 2024 | 27 | 2024 |
| Data-space inversion with a recurrent autoencoder for naturally fractured systems S Jiang, MH Hui, LJ Durlofsky Frontiers in Applied Mathematics and Statistics 7, 686754, 2021 | 15 | 2021 |
| Treatment of model error in subsurface flow history matching using a data-space method S Jiang, LJ Durlofsky Journal of Hydrology 603, 127063, 2021 | 14 | 2021 |
| Data-space inversion with variable well controls in the prediction period S Jiang Master’s thesis, Stanford University, 2018 | 6 | 2018 |
| Deep Neural Network Surrogate Flow Models for History Matching and Uncertainty Quantification S Jiang, L Durlofsky Machine Learning Applications in Subsurface Energy Resource Management, 271-290, 2022 | 2 | 2022 |
| Selecting appropriate model complexity: An example of tracer inversion for thermal prediction in enhanced geothermal systems H Wu, Z Jin, S Jiang, H Tang, JP Morris, J Zhang, B Zhang Water Resources Research 60 (7), e2023WR036146, 2024 | 1 | 2024 |
| A Data-Space Approach for Well Control Optimization under Uncertainty S Jiang, W Sun, LJ Durlofsky ECMOR XVI-16th European Conference on the Mathematics of Oil Recovery 2018 …, 2018 | 1 | 2018 |
| Prediction of Fault Slip Tendency in CO Storage using Data-space Inversion X He, S Jiang, LJ Durlofsky arXiv preprint arXiv:2601.05431, 2026 | | 2026 |
| Global assessment of seawater intrusion under uncertain climate scenarios using machine learning S Jiang, D Dwivedi AGU25, 2025 | | 2025 |
| Scalable Surrogate Modeling of Coastal Groundwater and Salinity Dynamics under Sea-Level Rise D Dwivedi, Y Zhang, S Jiang, C Liu, B Abylkhani, DM Tartakovsky AGU25, 2025 | | 2025 |
| Latent Score-Based Diffusion Model for Data Assimilation in Geological CO₂ Storage S Jiang AGU25, 2025 | | 2025 |
| Geofuse: An efficient surrogate model for seawater intrusion prediction and uncertainty reduction S Jiang, C Liu, D Dwivedi Water Resources Research 61 (9), e2024WR038898, 2025 | | 2025 |
| DL-NRS Net: A Physics-Informed Fourier Neural Operator Framework for High-Resolution Reconstruction Without High-Resolution Labels J Liu, H Pan, S Jiang, H Jing, Y Zhu Mathematical Geosciences, 1-27, 2025 | | 2025 |
| Enhancing Predictive Capabilities for Watershed Function through Transferability with ATS and FNO D Dwivedi, C Anand, S Jiang, U Mital, K Azizzadenesheli, P Shuai, Z Xu, ... AGU Fall Meeting Abstracts 2024 (998), H51T-0998, 2024 | | 2024 |
Louis DurlofskyProfessor of Energy Science and Engineering, Stanford UniversityVerified email at stanford.edu
