Deep learning of subsurface flow via theory-guided neural network N Wang, D Zhang, H Chang, H Li Journal of Hydrology 584, 124700, 2020 | 233 | 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 | 80 | 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 | 63 | 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 | 62 | 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 | 53 | 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 | 50 | 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 | 33 | 2022 |
Efficient Uncertainty Quantification and Data Assimilation via Theory-Guided Convolutional Neural Network N Wang, H Chang, D Zhang SPE Journal, 1-29, 2021 | 30 | 2021 |
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 | 28 | 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 | 27 | 2022 |
A Lagrangian dual-based theory-guided deep neural network M Rong, D Zhang, N Wang Complex & Intelligent Systems 8 (6), 4849-4862, 2022 | 19 | 2022 |
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 | 17 | 2022 |
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 | 15* | 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 | 15 | 2021 |
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 | 11 | 2021 |
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 | 9 | 2022 |
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 | 4 | 2023 |
压实与嵌入作用下压裂裂缝导流能力模型建立与影响因素分析 陈冬, 王楠哲, 叶智慧, 张佳亮 石油钻探技术 46 (6), 82-89, 2018 | 4 | 2018 |
GANSim-surrogate: An integrated framework for stochastic conditional geomodelling S Song, D Zhang, T Mukerji, N Wang Journal of Hydrology 620, 129493, 2023 | 3* | 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 | 2 | 2023 |