Imperial College London


Faculty of EngineeringDepartment of Earth Science & Engineering

Professorial Research Fellow



+44 (0)20 7594 9322c.pain




4.96Royal School of MinesSouth Kensington Campus





I am a Professor in the department of Earth Science and Engineering at Imperial College London (ICL), UK. I am also head of the Applied Computation and Modelling Group (AMCG), which is the largest department research group at ICL and comprises of about 70 research active scientists. AMCG specialises in the development and application of innovative and world leading modelling techniques for earth, engineering and biomedical sciences. The group has core research interests in numerical methods for ocean, atmosphere and climate systems, engineering fluids including multiphase flows, neutral particle radiation transport, coupled fluids-solids modelling with discrete element methods, turbulence modelling, inversion methods, imaging, and impact cratering. I was honoured by Imperial College London by winning its Research Excellence Award in 2010, in recognition my world-leading research.

As of 2017, I have authored over 200 journal publications (which have so far received over 5000 citations), 2 book chapters, 1 patent, graduated 45 PhD students and completed 40 industry and research council grants.

Career Highlights and Awards (as of 2017)

  • Published 200 journal papers, 2 book chapters, 1 patent, and 2 published reports.
  • Graduated 45 PhD students.
  • Rector’s Award for Research Excellence 2010 – only one awarded at Imperial annually.
  • Rector’s Award for Research 2004.
  • Attracted more than 40 industry, research council and European grants.
  • Attracted over £23M in research funding over the last 10 years.
  • Chair and organiser of a number of conferences e.g. Ocean Modelling, Transport Theory and the Japanese Todai Forum 2010.



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

Hamzehloo A, Bahlali ML, Salinas P, et al., 2022, Modelling saline intrusion using dynamic mesh optimization with parallel processing, Advances in Water Resources, Vol:164, ISSN:0309-1708

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

Wu P, Pan K, Ji L, et al., 2022, Navier-stokes Generative Adversarial Network: a physics-informed deep learning model for fluid flow generation, Neural Computing & Applications, Vol:34, ISSN:0941-0643, Pages:11539-11552

Cheng M, Fang F, Navon IM, et al., 2022, Spatio-Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: Autoencoder and Generative Adversarial Networks, Journal of Advances in Modeling Earth Systems, Vol:14

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