| Physics-informed semantic inpainting: Application to geostatistical modeling Q Zheng, L Zeng, GE Karniadakis Journal of Computational Physics 419, 109676, 2020 | 58 | 2020 |
| Surrogate‐based Bayesian inverse modeling of the hydrological system: An adaptive approach considering surrogate approximation error J Zhang, Q Zheng, D Chen, L Wu, L Zeng Water Resources Research 56 (1), e2019WR025721, 2020 | 43 | 2020 |
| Using deep learning to improve ensemble smoother: Applications to subsurface characterization J Zhang, Q Zheng, L Wu, L Zeng Water Resources Research 56 (12), e2020WR027399, 2020 | 33 | 2020 |
| Digital rock reconstruction with user-defined properties using conditional generative adversarial networks Q Zheng, D Zhang Transport in Porous Media 144 (1), 255-281, 2022 | 26 | 2022 |
| Adaptive multifidelity data assimilation for nonlinear subsurface flow problems Q Zheng, J Zhang, W Xu, L Wu, L Zeng Water Resources Research 55 (1), 203-217, 2019 | 26 | 2019 |
| RockGPT: reconstructing three-dimensional digital rocks from single two-dimensional slice with deep learning Q Zheng, D Zhang Computational Geosciences 26 (3), 677-696, 2022 | 15 | 2022 |
| Soil hydraulic parameters estimation using ground penetrating radar data via ensemble smoother with multiple data assimilation F Cui, J Bao, Z Cao, L Li, Q Zheng Journal of hydrology 583, 124552, 2020 | 15 | 2020 |
| Adaptive multi-fidelity probabilistic collocation-based Kalman filter for subsurface flow data assimilation: numerical modeling and real-world experiment J Man, Q Zheng, L Wu, L Zeng Stochastic environmental research and risk assessment 34, 1135-1146, 2020 | 10 | 2020 |
| Improving parameter estimation with an efficient sequential probabilistic collocation-based optimal design method J Man, Q Zheng, L Wu, L Zeng Journal of hydrology 569, 1-11, 2019 | 10 | 2019 |
| A probabilistic collocation based iterative Kalman filter for landfill data assimilation Q Zheng, W Xu, J Man, L Zeng, L Wu Advances in water resources 109, 170-180, 2017 | 5 | 2017 |
| State-space modeling for electrochemical performance of Li-ion batteries with physics-informed deep operator networks Q Zheng, X Yin, D Zhang Journal of Energy Storage 73, 109244, 2023 | 2 | 2023 |
| Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks Q Zheng, X Yin, D Zhang Journal of Energy Storage 65, 107176, 2023 | 2 | 2023 |
| Streambed water flux characterization through a Deep-Learning-Based approach considering data worth analysis: Numerical modeling and sandbox experiments L Ju, Y Hou, J Zhang, Q Zheng, H Yan Journal of Hydrology 612, 128111, 2022 | | 2022 |
Lingzao ZengZhejiang UniversityVerified email at zju.edu.cn
