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2020 – today
- 2024
[i13]Nanzhe Wang, Louis J. Durlofsky:
Deep Learning Framework for History Matching CO2 Storage with 4D Seismic and Monitoring Well Data. CoRR abs/2408.01575 (2024)- 2022
- [j4]Nanzhe Wang
, Haibin Chang, Dongxiao Zhang:
Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network. J. Comput. Phys. 466: 111419 (2022) - [i12]Nanzhe Wang, Qinzhuo Liao, Haibin Chang, Dongxiao Zhang:
Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network. CoRR abs/2201.00698 (2022) - [i11]Nanzhe Wang, Haibin Chang, Xiangzhao Kong, Martin O. Saar, Dongxiao Zhang:
Deep learning based closed-loop optimization of geothermal reservoir production. CoRR abs/2204.08987 (2022) - 2021
- [j3]Rui Xu, Dongxiao Zhang, Miao Rong, Nanzhe Wang:
Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow. J. Comput. Phys. 436: 110318 (2021) - [j2]Hao Xu, Dongxiao Zhang, Nanzhe Wang:
Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data. J. Comput. Phys. 445: 110592 (2021) - [j1]Yuntian Chen, Dou Huang, Dongxiao Zhang, Junsheng Zeng, Nanzhe Wang, Haoran Zhang, Jinyue Yan:
Theory-guided hard constraint projection (HCP): A knowledge-based data-driven scientific machine learning method. J. Comput. Phys. 445: 110624 (2021) - [i10]Nanzhe Wang, Haibin Chang, Dongxiao Zhang:
Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network. CoRR abs/2110.10080 (2021) - [i9]Rui Xu, Dongxiao Zhang, Nanzhe Wang:
Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network. CoRR abs/2111.08691 (2021) - 2020
- [i8]Nanzhe Wang, Haibin Chang, Dongxiao Zhang:
Efficient Uncertainty Quantification for Dynamic Subsurface Flow with Surrogate by Theory-guided Neural Network. CoRR abs/2004.13560 (2020) - [i7]Nanzhe Wang, Haibin Chang, Dongxiao Zhang:
Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example. CoRR abs/2007.15580 (2020) - [i6]Miao Rong, Dongxiao Zhang, Nanzhe Wang:
A Lagrangian Dual-based Theory-guided Deep Neural Network. CoRR abs/2008.10159 (2020) - [i5]Rui Xu, Dongxiao Zhang, Miao Rong, Nanzhe Wang:
Weak Form Theory-guided Neural Network (TgNN-wf) for Deep Learning of Subsurface Single and Two-phase Flow. CoRR abs/2009.04543 (2020) - [i4]Nanzhe Wang, Haibin Chang, Dongxiao Zhang:
Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling. CoRR abs/2011.08618 (2020) - [i3]Hao Xu, Dongxiao Zhang, Nanzhe Wang:
Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data. CoRR abs/2011.11981 (2020) - [i2]Yuntian Chen, Dou Huang, Dongxiao Zhang, Junsheng Zeng, Nanzhe Wang, Haoran Zhang, Jinyue Yan:
Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method. CoRR abs/2012.06148 (2020)
2010 – 2019
- 2019
- [i1]Nanzhe Wang, Dongxiao Zhang, Haibin Chang, Heng Li:
Deep Learning of Subsurface Flow via Theory-guided Neural Network. CoRR abs/1911.00103 (2019)
Coauthor Index
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