Imperial College London
Alternate

Professor Anil Anthony Bharath

Faculty of Engineering, Department of Bioengineering

Academic Director (Singapore)
 
 
 
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Contact

 

+44 (0)20 7594 5463a.bharath Website

 
 
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Location

 

4.12Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Fotiadis:2020,
author = {Fotiadis, S and Pignatelli, E and Valencia, ML and Cantwell, C and Storkey, A and Bharath, AA},
publisher = {arXiv},
title = {Comparing recurrent and convolutional neural networks for predicting  wave propagation},
url = {http://arxiv.org/abs/2002.08981v3},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - Dynamical systems can be modelled by partial differential equations andnumerical computations are used everywhere in science and engineering. In thiswork, we investigate the performance of recurrent and convolutional deep neuralnetwork architectures to predict the surface waves. The system is governed bythe Saint-Venant equations. We improve on the long-term prediction overprevious methods while keeping the inference time at a fraction of numericalsimulations. We also show that convolutional networks perform at least as wellas recurrent networks in this task. Finally, we assess the generalisationcapability of each network by extrapolating in longer time-frames and indifferent physical settings.
AU  - Fotiadis,S
AU  - Pignatelli,E
AU  - Valencia,ML
AU  - Cantwell,C
AU  - Storkey,A
AU  - Bharath,AA
PB  - arXiv
PY  - 2020///
TI  - Comparing recurrent and convolutional neural networks for predicting  wave propagation
UR  - http://arxiv.org/abs/2002.08981v3
UR  - http://hdl.handle.net/10044/1/79533
ER  -
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