Corpus ID: 221266564

Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers

@article{Liu2020HierarchicalDL,
  title={Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers},
  author={Yuying Liu and J. Kutz and S. Brunton},
  journal={ArXiv},
  year={2020},
  volume={abs/2008.09768}
}

Yuying Liu, J. Kutz, S. Brunton

Published 2020

Computer Science, Mathematics, Physics

ArXiv

Nonlinear differential equations rarely admit closed-form solutions, thus requiring numerical time-stepping algorithms to approximate solutions. Further, many systems characterized by multiscale physics exhibit dynamics over a vast range of timescales, making numerical integration computationally expensive due to numerical stiffness. In this work, we develop a hierarchy of deep neural network time-steppers to approximate the flow map of the dynamical system over a disparate range of time-scales… CONTINUE READING

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