| 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 | 68 | 2017 |
| A data-space inversion procedure for well control optimization and closed-loop reservoir management S Jiang, W Sun, LJ Durlofsky Computational Geosciences 24, 361-379, 2020 | 33 | 2020 |
| Data-space inversion using a recurrent autoencoder for time-series parameterization S Jiang, LJ Durlofsky Computational Geosciences 25 (1), 411-432, 2021 | 20 | 2021 |
| 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 | 19 | 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 | 17 | 2022 |
| 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 | 11 | 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 | 8 | 2021 |
| Data-space inversion with variable well controls in the prediction period S Jiang Master’s thesis, Stanford University, 2018 | 6 | 2018 |
| 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 | 3 | 2024 |
| 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 | 2* | 2024 |
| 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 |
| 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 |
| Data-space Inversion for Forecasting Flow and Geomechanical Quantities in CO2 Storage LJ Durlofsky, S Jiang, X He AGU23, 2023 | | 2023 |
| 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 Authorea Preprints, 2022 | | 2022 |
| Use of deep learning and error correction for data-space inversion and model-based history matching S Jiang Stanford University, 2022 | | 2022 |
| Transfer Learning Based Multi-fidelity Surrogate Model for Lithium-ion Battery Pack W Ma, S Jiang | | |
| A Transfer Learning-Based Surrogate Model for Geological Carbon Storage with Multi-Fidelity Training Data S Jiang, H Tang, P Fu, H Jo | | |
Louis DurlofskyProfessor of Energy Science and Engineering, Stanford University在 stanford.edu 的电子邮件经过验证
Louis DurlofskyProfessor of Energy Science and Engineering, Stanford University在 stanford.edu 的电子邮件经过验证