Corpus ID: 214802847

On the Convergence and generalization of Physics Informed Neural Networks

@article{Shin2020OnTC,
  title={On the Convergence and generalization of Physics Informed Neural Networks},
  author={Yeonjong Shin and J{\'e}r{\^o}me Darbon and George Em Karniadakis},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.01806}
}
  • Yeonjong Shin, Jérôme Darbon, George Em Karniadakis
  • Published 2020
  • Mathematics, Computer Science
  • ArXiv
  • Physics informed neural networks (PINNs) are deep learning based techniques for solving partial differential equations (PDEs). Guided by data and physical laws, PINNs find a neural network that approximates the solution to a system of PDEs. Such a neural network is obtained by minimizing a loss function in which any prior knowledge of PDEs and data are encoded. Despite its remarkable empirical success, there is little theoretical justification for PINNs. In this paper, we establish a… CONTINUE READING

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    References

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