Imperial College London
Alternate

Dr Chris Cantwell

Faculty of Engineering, Department of Aeronautics

Senior Lecturer in Aeronautics
 
 
 
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Contact

 

+44 (0)20 7594 5050c.cantwell Website

 
 
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Location

 

Department of Aeronautics, Room 219City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Lino:2020,
author = {Lino, M and Cantwell, C and Fotiadis, S and Pignatelli, E and Bharath, A},
publisher = {arXiv},
title = {Simulating surface wave dynamics with convolutional networks},
url = {http://arxiv.org/abs/2012.00718v1},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - We investigate the performance of fully convolutional networks to simulatethe motion and interaction of surface waves in open and closed complexgeometries. We focus on a U-Net architecture and analyse how well itgeneralises to geometric configurations not seen during training. Wedemonstrate that a modified U-Net architecture is capable of accuratelypredicting the height distribution of waves on a liquid surface within curvedand multi-faceted open and closed geometries, when only simple box andright-angled corner geometries were seen during training. We also consider aseparate and independent 3D CNN for performing time-interpolation on thepredictions produced by our U-Net. This allows generating simulations with asmaller time-step size than the one the U-Net has been trained for.
AU  - Lino,M
AU  - Cantwell,C
AU  - Fotiadis,S
AU  - Pignatelli,E
AU  - Bharath,A
PB  - arXiv
PY  - 2020///
TI  - Simulating surface wave dynamics with convolutional networks
UR  - http://arxiv.org/abs/2012.00718v1
UR  - http://hdl.handle.net/10044/1/85350
ER  -
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