Imperial College London


Faculty of EngineeringDepartment of Earth Science & Engineering

Eric and Wendy Schmidt AI in Science Postdoctoral Fellows, a



c.heaney Website




Royal School of MinesSouth Kensington Campus





My research interests span several areas including:
(1) reduced-order modelling;
(2) machine-learning for scientific applications;
(3) urban and environmental flows.

I am currently working as an AI in Science Research Fellow jointly at Imperial-X and the Department of Earth Science and Engineering, where I am investigating the use of green infrastructure to combat air pollution. 

I am a co-author of one of Wiley's top cited articles (Jan 2021 - Dec 2022): An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion (, which proposed using an autoencoder rather than proper orthogonal decomposition (POD) to obtain a low-dimensional space for a projection-based (or intrusive) reduced-order model. The autoencoder can capture the behaviour of a system with fewer degrees of freedom compared to POD.

Selected Publications

Journal Articles

Heaney C, Liu X, Go H, et al., 2022, Extending the capabilities of data-driven reduced-order models to make predictions for unseen scenarios: applied to flow around buildings, Frontiers in Physics, Vol:10, ISSN:2296-424X, Pages:1-16

Heaney CE, Wolffs Z, Tómasson JA, et al., 2022, An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes, Physics of Fluids, Vol:34, ISSN:1070-6631, Pages:1-22

Obeysekara A, Salinas P, Heaney CE, et al., 2021, Prediction of multiphase flows with sharp interfaces using anisotropic mesh optimisation, Advances in Engineering Software, Vol:160, ISSN:0965-9978, Pages:1-16

Heaney CE, Buchan AG, Pain CC, et al., 2021, Reduced-order modelling applied to the multigroup neutron diffusion equation using a nonlinear interpolation method for control-rod movement, Energies, ISSN:1996-1073

Quilodrán-Casas C, Silva VS, Arcucci R, et al., 2021, Digital twins based on bidirectional LSTM and GAN for modelling COVID-19

Heaney CE, Li Y, Matar OK, et al., 2020, Applying Convolutional Neural Networks to Data on Unstructured Meshes with Space-Filling Curves, Arxiv

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