88 citations · 482 across the 21 of their papers we have counts for
22 papers
Solving the Discretised Boltzmann Transport Equations using Neural Networks: Applications in Neutron Transport
T. R. F. Phillips, C. E. Heaney, C. Boyang +2
In this paper we solve the Boltzmann transport equation using AI libraries. The reason why this is attractive is because it enables one to use the highly optimised software within…
Solving the Discretised Neutron Diffusion Equations using Neural Networks
T. R. F. Phillips, C. E. Heaney, C. Boyang +2
This paper presents a new approach which uses the tools within Artificial Intelligence (AI) software libraries as an alternative way of solving partial differential equations (PDEs…
Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models
Sibo Cheng, Jianhua Chen, Charitos Anastasiou +5
Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional dynamical systems to improve t…
An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes
Claire E. Heaney, Zef Wolffs, Jón Atli Tómasson +7
The modelling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over…
Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19
Vinicius L. S. Silva, Claire E. Heaney, Yaqi Li +1
We propose the novel use of a generative adversarial network (GAN) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, w…
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty Quantification
Vinicius L. S. Silva, Claire E. Heaney, Nenko Nenov +1
We propose a new method in which a generative network (GN) integrate into a reduced-order model (ROM) framework is used to solve inverse problems for partial differential equations…
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations
César Quilodrán-Casas, Rossella Arcucci, Laetitia Mottet +2
This paper presents an approach to improve the forecast of computational fluid dynamics (CFD) simulations of urban air pollution using deep learning, and most specifically adversar…
Numerical study of COVID-19 spatial-temporal spreading in London
J. Zheng, X. Wu, F. Fang +7
Recent study reported that an aerosolised virus (COVID-19) can survive in the air for a few hours. It is highly possible that people get infected with the disease by breathing and…
Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic
César Quilodrán-Casas, Vinicius Santos Silva, Rossella Arcucci +3
The outbreak of the coronavirus disease 2019 (COVID-19) has now spread throughout the globe infecting over 150 million people and causing the death of over 3.2 million people. Thus…
Adversarially trained LSTMs on reduced order models of urban air pollution simulations
César Quilodrán-Casas, Rossella Arcucci, Christopher Pain +1
This paper presents an approach to improve computational fluid dynamics simulations forecasts of air pollution using deep learning. Our method, which integrates Principal Component…
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…
Goal-based angular adaptivity for Boltzmann transport in the presence of ray-effects
S. Dargaville, R. P. Smedley-Stevenson, P. N. Smith +1
Boltzmann transport problems often involve heavy streaming, where particles propagate long distance due to the dominance of advection over particle interaction. If an insufficientl…
Angular adaptivity with spherical harmonics for Boltzmann transport
S. Dargaville, A. G. Buchan, R. P. Smedley-Stevenson +2
This paper describes an angular adaptivity algorithm for Boltzmann transport applications which uses Pn and filtered Pn expansions, allowing for different expansion orders across s…
Scalable angular adaptivity for Boltzmann transport
S. Dargaville, A. G. Buchan, R. P. Smedley-Stevenson +2
This paper describes an angular adaptivity algorithm for Boltzmann transport applications which for the first time shows evidence of scaling in both runtime and me…
Goal-based sensitivity maps using time windows and ensemble perturbations
C. E. Heaney, P. Salinas, F. Fang +2
We present an approach for forming sensitivity maps (or sensitivites) using ensembles. The method is an alternative to using an adjoint, which can be very challenging to formulate…
Solving the Poisson equation on small aspect ratio domains using unstructured meshes
S. C. Kramer, C. J. Cotter, C. C. Pain
We discuss the ill conditioning of the matrix for the discretised Poisson equation in the small aspect ratio limit, and motivate this problem in the context of nonhydrostatic ocean…
LBB Stability of a Mixed Discontinuous/Continuous Galerkin Finite Element Pair
C. J. Cotter, D. A. Ham, C. C. Pain +1
We introduce a new mixed discontinuous/continuous Galerkin finite element for solving the 2- and 3-dimensional wave equations and equations of incompressible flow. The element, whi…