329 citations · 939 across the 14 of their papers we have counts for
4 papers · 1 filter
Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network
Nanzhe Wang, Qinzhuo Liao, Haibin Chang +1
Large-scale or high-resolution geologic models usually comprise a huge number of grid blocks, which can be computationally demanding and time-consuming to solve with numerical simu…
Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network
Rui Xu, Dongxiao Zhang, Nanzhe Wang
We build surrogate models for dynamic 3D subsurface single-phase flow problems with multiple vertical producing wells. The surrogate model provides efficient pressure estimation of…
Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network
Nanzhe Wang, Haibin Chang, Dongxiao Zhang
The theory-guided convolutional neural network (TgCNN) framework, which can incorporate discretized governing equation residuals into the training of convolutional neural networks…
Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method
Yuntian Chen, Dou Huang, Dongxiao Zhang +4
Machine learning models have been successfully used in many scientific and engineering fields. However, it remains difficult for a model to simultaneously utilize domain knowledge…