Publications
Journal Publications
[10] S. Jiang, and L. J. Durlofsky. History Matching for Geological Carbon Storage using Data-Space Inversion with Spatio-Temporal Data Parameterization, International Journal of Greenhouse Gas Control (2024).
[9] Y. Han, F. P. Hamon, S. Jiang, and L. J. Durlofsky. Surrogate Model for CO2 Storage and Its Use in MCMC-based History Matching, Advances in Water Resources (2024).
[8] H. Wu, Z. Jin, S. Jiang, H. Tang, J. P. Morris, J. Zhang, and B. Zhang. Selecting Appropriate Model Complexity: An Example of Tracer Inversion for Thermal Prediction in Enhanced Geothermal Systems, Water Resources Research (2024).
[7] S. Jiang, and L. J. Durlofsky. Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models. Journal of Computational Physics (2023).
[6] H. Tang, P. Fu, H. Jo, S. Jiang, C. S. Sherman, F. Hamon, N. A. Azzolia, and J. P. Morris. (co-corresponding author) Deep Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSAR. International Journal of Greenhouse Gas Control (2022).
[5] S. Jiang, and L. J. Durlofsky. Treatment of Model Error in Subsurface Flow History Matching using a Data-Space Method. Journal of Hydrology (2021).
[4] S. Jiang, M. Hui, and L. J. Durlofsky. Application of RAE-based Data-space Inversion for a Naturally Fractured Reservoir. Frontiers in Applied Mathematics and Statistics (2021).
[3] S. Jiang, and L. J. Durlofsky. Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization. Computational Geosciences (2021).
[2] S. Jiang, W. Sun, and L. J. Durlofsky. A Data-Space Inversion Procedure for Well Control Optimization and Closed-Loop Reservoir Management. Computational Geosciences (2020).
[1] Y. Chen, S. Jiang, D. Zhang and C. Liu. (co-first author) An Adsorbed Gas Estimation Model for Shale Gas Reservoirs via Statistical Learning. Applied Energy (2017).
Under Review and in Preparation
[5] S. Jiang, C. Liu, and D. Dwivedi. GeoFUSE: A High-Efficiency Surrogate Model for Seawater Intrusion Prediction and Uncertainty Reduction, submitted to Water Resources Research.
[4] S. Jiang, C. Liu, D. Dwivedi, and D. Tartakovsky. Enhancing Predictive Capabilities for Seawater Intrusion over U.S. through Transfer Learning, in preparation.
[3] S. Jiang, W. Ma, S. Onori, and L. J. Durlofsky. Surrogate Model and Uncertainty Quantification for Lithium-ion Battery Pack Performance, in preparation.
[2] X. He, S. Jiang and L. J. Durlofsky. Data-Space Inversion for Prediction of Fault Slip Tendency in CO2 Storage, in preparation.
[1] J. Zhao, S. Jiang, and D. Zhang. Mechanical Classification of Organic-Rich Shale Based on High-Speed Nanoindentation and Machine Learning, to be submitted to Journal of Geophysical Research: Solid Earth.
Book Chapters
[1] S. Jiang, and L. J. Durlofsky. Deep-Neural-Network Surrogate Flow Models for History Matching and Uncertainty Quantification, in Machine Learning Applications in Subsurface Energy Resource Management: State of the Art and Future Prognosis, Chp. 14, S. Mishra, ed., CRC Press (2022).
Conference Proceedings
[2] S. Jiang, H. Tang, P. Fu, and H. Jo. A Transfer Learning-Based Surrogate Model for Geological Carbon Storage with Multi-Fidelity Training Data. NeurIPS 2021 Workshop, Tackling Climate Change with Machine Learning (2021).
[1] S. Jiang, W. Sun, and L. J. Durlofsky. A Data-Space Approach for Well Control Optimization under Uncertainty. ECMOR XVI-16th European Conference on the Mathematics of Geological Reservoirs (2018), Barcelona, Spain.