Imperial College London

ProfessorChristopherPain

Faculty of EngineeringDepartment of Earth Science & Engineering

Professorial Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 9322c.pain

 
 
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Location

 

3.48Royal School of MinesSouth Kensington Campus

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Summary

 

Summary

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.

Publications

Journals

Arcucci R, Mottet L, Pain C, et al., 2019, Optimal reduced space for Variational Data Assimilation, Journal of Computational Physics, Vol:379, ISSN:0021-9991, Pages:51-69

Xiao D, Heaney CE, Mottet L, et al., 2019, A reduced order model for turbulent flows in the urban environment using machine learning, Building and Environment, Vol:148, ISSN:0360-1323, Pages:323-337

Xiao D, Du J, Fang F, et al., 2018, Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation, Computers and Fluids, Vol:177, ISSN:0045-7930, Pages:69-77

Lei Q, Xie Z, Pavlidis D, et al., 2018, The shape and motion of gas bubbles in a liquid flowing through a thin annulus, Journal of Fluid Mechanics, Vol:285, ISSN:0022-1120, Pages:1017-1039

Obeysekara A, Lei Q, Salinas P, et al., 2018, Modelling stress-dependent single and multi-phase flows in fractured porous media based on an immersed-body method with mesh adaptivity, Computers and Geotechnics, Vol:103, ISSN:0266-352X, Pages:229-241

More Publications