Publications

Journal Publications

• Nanzhe Wang; Yuntian Chen; Dongxiao Zhang (2025). A comprehensive review of physics-informed deep learning and its applications in geoenergy development. The Innovation Energy, 2(2): 100087-1-100087-15.
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• Nanzhe Wang; Louis J. Durlofsky (2025). Deep learning framework for history matching CO2 storage with 4D seismic and monitoring well data. Geoenergy Science and Engineering, 248: 213736.
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• Nanzhe Wang; Xiang‐Zhao Kong; Dongxiao Zhang (2024). Physics‐Informed Convolutional Decoder (PICD): A novel approach for direct inversion of heterogeneous subsurface flow. Geophysical Research Letters, 51(13): e2024GL108163.
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• Nanzhe Wang; Qinzhuo Liao; Haibin Chang; Dongxiao Zhang (2023). Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network. Computational Geosciences, 27(6): 913-938.
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• Nanzhe Wang; Haibin Chang; Dongxiao Zhang (2023). Inverse modeling for subsurface flow based on deep learning surrogates and active learning strategies. Water Resources Research, 59(7): e2022WR033644.
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• Nanzhe Wang; Haibin Chang; Xiang-Zhao Kong; Dongxiao Zhang (2023). Deep learning based closed-loop well control optimization of geothermal reservoir with uncertain permeability. Renewable Energy, 211: 379-394.
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• Nanzhe Wang; Haibin Chang; Dongxiao Zhang (2022). Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network. Journal of Computational Physics, 466: 111419.
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• Nanzhe Wang; Haibin Chang; Dongxiao Zhang; Liang Xue; Yuntian Chen (2022). Efficient well placement optimization based on theory-guided convolutional neural network. Journal of Petroleum Science and Engineering, 208: 109545.
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• Nanzhe Wang; Haibin Chang; Dongxiao Zhang (2021). Efficient uncertainty quantification and data assimilation via theory-guided convolutional neural network. SPE Journal, 26(6): 4128–4156.
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• Nanzhe Wang; Haibin Chang; Dongxiao Zhang (2021). Theory-guided auto-encoder for surrogate construction and inverse modeling. Computer Methods in Applied Mechanics and Engineering, 385: 114037.
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• Nanzhe Wang; Haibin Chang; Dongxiao Zhang (2021). Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network. Computer Methods in Applied Mechanics and Engineering, 373: 113492.
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• Nanzhe Wang; Haibin Chang; Dongxiao Zhang (2021). Deep-learning-based inverse modeling approaches: A subsurface flow example. Journal of Geophysical Research: Solid Earth, 126(2): e2020JB020549.
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• Nanzhe Wang; Dongxiao Zhang; Haibin Chang; Heng Li (2020). Deep learning of subsurface flow via theory-guided neural network. Journal of Hydrology, 584: 124700.
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