Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations

@article{Shi2020FiniteDN,
  title={Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations},
  author={Zheng Shi and Nur Sila Gulgec and Albert S. Berahas and Shamim N. Pakzad and Martin Tak{\'a}c},
  journal={2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)},
  year={2020},
  pages={130-135}
}
  • Zheng Shi, Nur Sila Gulgec, M. Takác
  • Published 2 June 2020
  • Computer Science, Mathematics
  • 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
Discovering the underlying behavior of complex systems is an important topic in many science and engineering disciplines. In this paper, we propose a novel neural network framework, finite difference neural networks (FD-Net), to learn partial differential equations from data. Specifically, our proposed finite difference inspired network is designed to learn the underlying governing partial differential equations from trajectory data, and to iteratively estimate the future dynamical behavior… 
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