| Deep convolutional encoder‐decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media S Mo, Y Zhu, N Zabaras, X Shi, J Wu Water Resources Research 55 (1), 703-728, 2019 | 406 | 2019 |
| Deep autoregressive neural networks for high‐dimensional inverse problems in groundwater contaminant source identification S Mo, N Zabaras, X Shi, J Wu Water Resources Research 55 (5), 3856-3881, 2019 | 291 | 2019 |
| Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non‐Gaussian hydraulic conductivities S Mo, N Zabaras, X Shi, J Wu Water Resources Research 56 (2), e2019WR026082, 2020 | 145 | 2020 |
| Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap S Mo, Y Zhong, E Forootan, N Mehrnegar, X Yin, J Wu, W Feng, X Shi Journal of Hydrology 604, 127244, 2022 | 115 | 2022 |
| A Taylor expansion‐based adaptive design strategy for global surrogate modeling with applications in groundwater modeling S Mo, D Lu, X Shi, G Zhang, M Ye, J Wu, J Wu Water Resources Research 53 (12), 10802-10823, 2017 | 79 | 2017 |
| Hydrogeophysical Characterization of Nonstationary DNAPL Source Zones by Integrating a Convolutional Variational Autoencoder and Ensemble Smoother X Kang, A Kokkinaki, PK Kitanidis, X Shi, J Lee, S Mo, J Wu Water Resources Research 57 (2), e2020WR028538, 2021 | 62 | 2021 |
| An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling S Mo, X Shi, D Lu, M Ye, J Wu Computers & Geosciences 125, 69-77, 2019 | 38 | 2019 |
| Hydrological Droughts of 2017–2018 Explained by the Bayesian Reconstruction of GRACE (‐FO) Fields S Mo, Y Zhong, E Forootan, X Shi, W Feng, X Yin, J Wu Water Resources Research 58 (9), e2022WR031997, 2022 | 36 | 2022 |
| Deep learning based optimization under uncertainty for surfactant-enhanced DNAPL remediation in highly heterogeneous aquifers J Du, X Shi, S Mo, X Kang, J Wu Journal of Hydrology 608, 127639, 2022 | 17 | 2022 |
| Deep learning-based geological parameterization for history matching CO2 plume migration in complex aquifers L Feng, S Mo, AY Sun, D Wang, Z Yang, Y Chen, H Wang, J Wu, X Shi Advances in Water Resources 193, 104833, 2024 | 9 | 2024 |
| Uncertainty quantification of CO2 plume migration in highly channelized aquifers using probabilistic convolutional neural networks L Feng, S Mo, AY Sun, J Wu, X Shi Advances in Water Resources 183, 104607, 2024 | 8 | 2024 |
| Water storage changes (2003–2020) in the Ordos Basin, China, explained by GRACE data and interpretable deep learning Z Hu, S Tang, S Mo, X Shi, X Yin, Y Sun, X Liu, L Duan, P Miao, T Liu, ... Hydrogeology Journal 32 (1), 307-320, 2024 | 7 | 2024 |
| Near‐Real‐Time Monitoring of Global Terrestrial Water Storage Anomalies and Hydrological Droughts S Mo, M Schumacher, AIJM van Dijk, X Shi, J Wu, E Forootan Geophysical Research Letters 52 (7), e2024GL112677, 2025 | 5 | 2025 |
| An explainable Bayesian TimesNet for probabilistic groundwater level prediction Z Peng, S Mo, AY Sun, J Wu, X Zeng, M Lu, X Shi Water Resources Research 61 (6), e2025WR040191, 2025 | 1 | 2025 |
| Leakage Risk Assessment in Geologic Carbon Sequestration using a Physics-Aware ConvLSTM Surrogate Model J Kang, X Shi, S Mo, AY Sun, L Wang, H Wang, J Wu Advances in Water Resources, 105017, 2025 | 1 | 2025 |
Xiaoqing Shi（施小清）Professor of Hydrogeology, Nanjing UniversityVerified email at nju.edu.cn
