• Corpus ID: 211252449

Comparing recurrent and convolutional neural networks for predicting wave propagation

@article{Fotiadis2020ComparingRA,
  title={Comparing recurrent and convolutional neural networks for predicting wave propagation},
  author={Stathi Fotiadis and Eduardo Pignatelli and Mario Lino Valencia and Chris D. Cantwell and Amos J. Storkey and Anil Anthony Bharath},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.08981}
}
Dynamical systems can be modelled by partial differential equations and numerical computations are used everywhere in science and engineering. In this work, we investigate the performance of recurrent and convolutional deep neural network architectures to predict the surface waves. The system is governed by the Saint-Venant equations. We improve on the long-term prediction over previous methods while keeping the inference time at a fraction of numerical simulations. We also show that… 
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