• Corpus ID: 219708206

Learning continuous-time PDEs from sparse data with graph neural networks

@article{Iakovlev2021LearningCP,
  title={Learning continuous-time PDEs from sparse data with graph neural networks},
  author={Valerii Iakovlev and Markus Heinonen and Harri L{\"a}hdesm{\"a}ki},
  journal={ArXiv},
  year={2021},
  volume={abs/2006.08956}
}
The behavior of many dynamical systems follow complex, yet still unknown partial differential equations (PDEs). While several machine learning methods have been proposed to learn PDEs directly from data, previous methods are limited to discrete-time approximations or make the limiting assumption of the observations arriving at regular grids. We propose a general continuous-time differential model for dynamical systems whose governing equations are parameterized by message passing graph neural… 

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