• Corpus ID: 219530610

RoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous Data

  title={RoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous Data},
  author={Shiying Xiong and Xingzhe He and Yunjin Tong and Runze Liu and Bo Zhu},
Predicting future discontinuous phenomena that are unobservable from the training data sets has been a challenging problem for scientific machine learning. In this paper, we introduce a novel learning paradigm to predict the emergence and evolution of various kinds of discontinuities for hyperbolic dynamic systems based on smooth observation data. At the heart of our approach is a templaterizable and data-driven Riemann solver that functions as a strong inductive prior to tackle the potential… 

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