Semi-supervised Learning of Partial Differential Operators and Dynamical Flows

@article{Rotman2022SemisupervisedLO,
  title={Semi-supervised Learning of Partial Differential Operators and Dynamical Flows},
  author={Michael Rotman and Amit Dekel and Ran Ilan Ber and Lior Wolf and Yaron Oz},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.14366}
}
The evolution of dynamical systems is generically governed by nonlinear partial differential equations (PDEs), whose solution, in a simulation framework, requires vast amounts of computational resources. In this work, we present a novel method that combines a hyper-network solver with a Fourier Neural Operator architecture. Our method treats time and space separately. As a result, it successfully propagates initial conditions in continuous time steps by employing the general composition… 
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