In this section
@inproceedings{Lino:2022,
author = {Lino, M and Fotiadis, S and Bharath, AA and Cantwell, C},
pages = {1--11},
publisher = {ICLR},
title = {Towards fast simulation of environmental fluid mechanics with multi-scale graph neural networks},
url = {http://arxiv.org/abs/2205.02637v1},
year = {2022}
}
TY - CPAPER
AB - Numerical simulators are essential tools in the study of naturalfluid-systems, but their performance often limits application in practice.Recent machine-learning approaches have demonstrated their ability toaccelerate spatio-temporal predictions, although, with only moderate accuracyin comparison. Here we introduce MultiScaleGNN, a novel multi-scale graphneural network model for learning to infer unsteady continuum mechanics inproblems encompassing a range of length scales and complex boundary geometries.We demonstrate this method on advection problems and incompressible fluiddynamics, both fundamental phenomena in oceanic and atmospheric processes. Ourresults show good extrapolation to new domain geometries and parameters forlong-term temporal simulations. Simulations obtained with MultiScaleGNN arebetween two and four orders of magnitude faster than those on which it wastrained.
AU - Lino,M
AU - Fotiadis,S
AU - Bharath,AA
AU - Cantwell,C
EP - 11
PB - ICLR
PY - 2022///
SP - 1
TI - Towards fast simulation of environmental fluid mechanics with multi-scale graph neural networks
UR - http://arxiv.org/abs/2205.02637v1
UR - https://iclr.cc/
UR - http://hdl.handle.net/10044/1/96796
ER -
Prof Anil Anthony Bharath
a.bharath@imperial.ac.uk
Telephone
+44 (0)20 7594 5463
Address
Room 4.12
Department of Bioengineering
Royal School of Mines Building
Imperial College London
