mlsanityData to 7 Oct 2026

Comparing recurrent and convolutional neural networks for predicting wave propagation

arXiv 2002.08981 ↗First posted 20 Feb 2020cs.LG cs.CV stat.MLcited by 15

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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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2020.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 2; 2024: 1.

15 papers.

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  1. 2022Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNNcited by 43
  2. 2021Simulating Continuum Mechanics with Multi-Scale Graph Neural Networkscited by 39
  3. 2021Effects of boundary conditions in fully convolutional networks for learning spatio-temporal dynamicscited by 14
  4. 2022Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networkscited by 11
  5. 2021Disentangled Generative Models for Robust Prediction of System Dynamicscited by 8
  6. 2021Physics-Aware Downsampling with Deep Learning for Scalable Flood Modelingcited by 4
  7. 2021On the reproducibility of fully convolutional neural networks for modeling time-space evolving physical systemscited by 3
  8. 2024Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Netcited by 3
  9. 2023Predictions Based on Pixel Data: Insights from PDEs and Finite Differencescited by 2
  10. 2022Estimation of fibre architecture and scar in myocardial tissue using electrograms: an in-silico studycited by 1
  11. 2022Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping processcited by 1
  12. 2022Development of a deep learning platform for optimising sheet stamping geometries subject to manufacturing constraintscited by 0
3 more
  1. 2022Assessment of convolutional recurrent autoencoder network for learning wave propagationcited by 0
  2. 2022Data-driven and machine-learning based prediction of wave propagation behavior in dam-break floodcited by 0
  3. 2023Stability of implicit neural networks for long-term forecasting in dynamical systemscited by 0