# Comparing recurrent and convolutional neural networks for predicting wave propagation

arXiv: https://arxiv.org/abs/2002.08981 · HTML: https://mlsanity.com/paper/2002.08981/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 15 of 15)

- [Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN](https://mlsanity.com/paper/2210.02573/) (2022)
- [Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks](https://mlsanity.com/paper/2106.04900/) (2021)
- [Effects of boundary conditions in fully convolutional networks for learning spatio-temporal dynamics](https://mlsanity.com/paper/2106.11160/) (2021)
- [Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks](https://mlsanity.com/paper/2205.02637/) (2022)
- [Disentangled Generative Models for Robust Prediction of System Dynamics](https://mlsanity.com/paper/2108.11684/) (2021)
- [Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling](https://mlsanity.com/paper/2106.07218/) (2021)
- [On the reproducibility of fully convolutional neural networks for modeling time-space evolving physical systems](https://mlsanity.com/paper/2105.05482/) (2021)
- [Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net](https://mlsanity.com/paper/2404.08091/) (2024)
- [Predictions Based on Pixel Data: Insights from PDEs and Finite Differences](https://mlsanity.com/paper/2305.00723/) (2023)
- [Estimation of fibre architecture and scar in myocardial tissue using electrograms: an in-silico study](https://mlsanity.com/paper/2212.03012/) (2022)
- [Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process](https://mlsanity.com/paper/2212.05885/) (2022)
- [Development of a deep learning platform for optimising sheet stamping geometries subject to manufacturing constraints](https://mlsanity.com/paper/2202.03422/) (2022)
- [Assessment of convolutional recurrent autoencoder network for learning wave propagation](https://mlsanity.com/paper/2204.05573/) (2022)
- [Data-driven and machine-learning based prediction of wave propagation behavior in dam-break flood](https://mlsanity.com/paper/2209.08729/) (2022)
- [Stability of implicit neural networks for long-term forecasting in dynamical systems](https://mlsanity.com/paper/2305.17155/) (2023)

## Abstract (arXiv)

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.