| Deep learning of subsurface flow via theory-guided neural network N Wang, D Zhang, H Chang, H Li Journal of Hydrology 584, 124700, 2020 | 284 | 2020 |
| Theory-guided hard constraint projection (HCP): A knowledge-based data-driven scientific machine learning method Y Chen, D Huang, D Zhang, J Zeng, N Wang, H Zhang, J Yan Journal of Computational Physics 445, 110624, 2021 | 119 | 2021 |
| Deep‐learning‐based inverse modeling approaches: A subsurface flow example N Wang, H Chang, D Zhang Journal of Geophysical Research: Solid Earth 126 (2), e2020JB020549, 2021 | 76 | 2021 |
| Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network N Wang, H Chang, D Zhang Computer Methods in Applied Mechanics and Engineering 373, 113492, 2021 | 74 | 2021 |
| Theory-guided auto-encoder for surrogate construction and inverse modeling N Wang, H Chang, D Zhang Computer Methods in Applied Mechanics and Engineering 385, 114037, 2021 | 67 | 2021 |
| Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single-and two-phase flow R Xu, D Zhang, M Rong, N Wang Journal of Computational Physics 436, 110318, 2021 | 60 | 2021 |
| Efficient well placement optimization based on theory-guided convolutional neural network N Wang, H Chang, D Zhang, L Xue, Y Chen Journal of Petroleum Science and Engineering 208, 109545, 2022 | 45 | 2022 |
| Efficient Uncertainty Quantification and Data Assimilation via Theory-Guided Convolutional Neural Network N Wang, H Chang, D Zhang SPE Journal, 1-29, 2021 | 44 | 2021 |
| Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network N Wang, H Chang, D Zhang Journal of Computational Physics 466, 111419, 2022 | 39 | 2022 |
| Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data H Xu, D Zhang, N Wang Journal of Computational Physics 445, 110592, 2021 | 33 | 2021 |
| Deep learning based closed-loop well control optimization of geothermal reservoir with uncertain permeability N Wang, H Chang, XZ Kong, D Zhang Renewable Energy 211, 379-394, 2023 | 27* | 2023 |
| Theory-guided full convolutional neural network: An efficient surrogate model for inverse problems in subsurface contaminant transport T He, N Wang, D Zhang Advances in Water Resources 157, 104051, 2021 | 25 | 2021 |
| Deep learning of two-phase flow in porous media via theory-guided neural networks J Li, D Zhang, N Wang, H Chang SPE Journal 27 (02), 1176-1194, 2022 | 24 | 2022 |
| A Lagrangian dual-based theory-guided deep neural network M Rong, D Zhang, N Wang Complex & Intelligent Systems 8 (6), 4849-4862, 2022 | 21 | 2022 |
| Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network R Xu, D Zhang, N Wang Journal of Hydrology 613, 128321, 2022 | 14 | 2022 |
| Solution of diffusivity equations with local sources/sinks and surrogate modeling using weak form theory-guided neural network R Xu, N Wang, D Zhang Advances in water resources 153, 103941, 2021 | 14 | 2021 |
| GANSim-surrogate: An integrated framework for stochastic conditional geomodelling S Song, D Zhang, T Mukerji, N Wang Journal of Hydrology 620, 129493, 2023 | 12 | 2023 |
| Inverse modeling for subsurface flow based on deep learning surrogates and active learning strategies N Wang, H Chang, D Zhang Water Resources Research 59 (7), e2022WR033644, 2023 | 10 | 2023 |
| Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network N Wang, Q Liao, H Chang, D Zhang Computational Geosciences 27 (6), 913-938, 2023 | 8 | 2023 |
| Physics‐Informed Convolutional Decoder (PICD): A novel approach for direct inversion of heterogeneous subsurface flow N Wang, XZ Kong, D Zhang Geophysical Research Letters 51 (13), e2024GL108163, 2024 | 4 | 2024 |
Dongxiao ZhangPeking UniversityEmail yang diverifikasi di pku.edu.cn