74 citations · 233 across the 18 of their papers we have counts for
20 papers
Inpainting U-Net for seamless pedestrian-level wind prediction across urban morphologies
Jingzi Huang, Claire E. Heaney, Tao Li +3
Pedestrian-level wind prediction is essential for urban design and wind-comfort assessment, but high-fidelity simulations such as LES remain computationally expensive for rapid eva…
NeuralFVM: Neural-physics-based Finite Volume Method for Turbulent Flows Using the - Model
Tingkai Xue, Yu Jiao, Te Ba +9
In this work, we develop a neural-physics solver based on finite volume method (FVM), namely NeuralFVM, for turbulent flows by implementing the standard - model designed for…
Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks
Jucai Zhai, Muhammad Abdullah, Boyang Chen +6
Neural Physics recasts local discretisations of partial differential equations (PDEs) as fixed convolutional operators, providing a physics-preserving alternative to data-driven su…
Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach
Nathalie C. Pinheiro, Donghu Guo, Hannah P. Menke +4
Modelling rock-fluid interaction requires solving a set of partial differential equations (PDEs) to predict the flow behaviour and the reactions of the fluid with the rock on the i…
An AI-driven framework for the prediction of personalised health response to air pollution
Nazanin Zounemat-Kermani, Sadjad Naderi, Claire H. Dilliway +10
Air pollution is a growing global health threat, exacerbated by climate change and linked to cardiovascular and respiratory diseases. While personal sensing devices enable real-tim…
Parameter estimation for land-surface models using Neural Physics
Ruiyue Huang, Claire E. Heaney, Maarten van Reeuwijk
We propose a novel inverse-modelling approach that estimates the parameters of a simple land-surface model (LSM) by assimilating data into a differentiable, physics-based forward m…
Online learning to accelerate nonlinear PDE solvers: applied to multiphase porous media flow
Vinicius L S Silva, Pablo Salinas, Claire E Heaney +2
We propose a novel type of nonlinear solver acceleration for systems of nonlinear partial differential equations (PDEs) that is based on online/adaptive learning. It is applied in…
Rapid modelling of reactive transport in porous media using machine learning: limitations and solutions
Vinicius L S Silva, Geraldine Regnier, Pablo Salinas +3
Reactive transport in porous media plays a pivotal role in subsurface reservoir processes, influencing fluid properties and geochemical characteristics. However, coupling fluid flo…
Solving the Discretised Multiphase Flow Equations with Interface Capturing on Structured Grids Using Machine Learning Libraries
Boyang Chen, Claire E. Heaney, Jefferson L. M. A. Gomes +2
This paper solves the discretised multiphase flow equations using tools and methods from machine-learning libraries. The idea comes from the observation that convolutional layers c…
Neural Physics: Using AI Libraries to Develop Physics-Based Solvers for Incompressible Computational Fluid Dynamics
Boyang Chen, Claire E. Heaney, Christopher C. Pain
Numerical discretisations of partial differential equations (PDEs) can be written as discrete convolutions, which, themselves, are a key tool in AI libraries and used in convolutio…
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…
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…
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…
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…
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…
On stability, convergence and accuracy of bES-FEM and bFS-FEM for nearly incompressible elasticity
Thanh Hai Ong, Claire E. Heaney, Chang-Kye Lee +2
We present in this paper a rigorous theoretical framework to show stability, convergence and accuracy of improved edge-based and face-based smoothed finite element methods (bESFEM…