Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model

@article{Li2022UncertaintyQO,
  title={Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model},
  author={Jun Yu Li and Dongxiao Zhang and Tianhao He and Qiang Zheng},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.14301}
}
Uncertainty quantification (UQ) of subsurface two-phase flow usually requires numerous executions of forward simulations under varying conditions. In this work, a novel coupled theory-guided neural network (TgNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgNN model in a coupled form, which reflects the… 

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