Comparing recurrent and convolutional neural networks for predicting wave propagation
This work addresses wave prediction for scientific and engineering applications, but it is incremental as it compares existing architectures without introducing a new method.
The authors tackled the problem of predicting wave propagation governed by Saint-Venant equations by comparing recurrent and convolutional neural networks, achieving improved long-term prediction over previous methods with faster inference than numerical simulations.
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 convolutional networks perform at least as well as recurrent networks in this task. Finally, we assess the generalisation capability of each network by extrapolating in longer time-frames and in different physical settings.