88 citations · 482 across the 21 of their papers we have counts for
6 papers · 1 filter
Data Assimilation in the Latent Space of a Neural Network
Maddalena Amendola, Rossella Arcucci, Laetitia Mottet +5
There is an urgent need to build models to tackle Indoor Air Quality issue. Since the model should be accurate and fast, Reduced Order Modelling technique is used to reduce the dim…
Applying Convolutional Neural Networks to Data on Unstructured Meshes with Space-Filling Curves
Claire E. Heaney, Yuling Li, Omar K. Matar +1
This paper presents the first classical Convolutional Neural Network (CNN) that can be applied directly to data from unstructured finite element meshes or control volume grids. CNN…
An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion
Toby Phillips, Claire E. Heaney, Paul N. Smith +1
Using an autoencoder for dimensionality reduction, this paper presents a novel projection-based reduced-order model for eigenvalue problems. Reduced-order modelling relies on findi…
A comparison of element agglomeration algorithms for unstructured geometric multigrid
S. Dargaville, A. G. Buchan, R. P. Smedley-Stevenson +2
This paper compares the performance of seven different element agglomeration algorithms on unstructured triangular/tetrahedral meshes when used as part of a geometric multigrid. Fi…
An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling
M. Cheng, F. Fang, C. C. Pain +1
Considering the high computation cost produced in conventional computation fluid dynamic simulations, machine learning methods have been introduced to flow dynamic simulations in r…
Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network
M. Cheng, F. Fang, C. C. Pain +1
Deep learning techniques for improving fluid flow modelling have gained significant attention in recent years. Advanced deep learning techniques achieve great progress in rapidly p…