Imperial College London


Faculty of EngineeringDepartment of Earth Science & Engineering

Senior Research Fellow



+44 (0)20 7594 1912f.fang CV




4.90Royal School of MinesSouth Kensington Campus





My research areas focus on  predictive modelling (data assimilation methods, model reduction and optimal controls) in geophysical models as well as applications in ocean, atmospheric, multiphase flows and environmental problems. The application areas I mostly look at are environmental issues (air pollution), natural hazard (flooding, for example), ocean and engineering problems.

Predictive modelling framework

The top animation is obtained from the high fidelity unstructured mesh finite element model; the bottom animation is obtained from the rapid reduced order model (ROM). Without compromising the solution's accuracy the ROM model was able to reduce the problem size by several orders of magnitude.

The application areas focus on:

  • Ocean and climate prediction system (numerical modelling and 4-D data assimilation and in particular: adaptive unstructured mesh; adaptive observations; second-order adjoint methods for assimilating data and targeted observations; goal-based error measures using adjoint analysis methods; reduced-order modelling and its applications to PDE constrained optimization; sensitivity analysis; uncertainty analysis and modelling)
  • Multi-physics fluid modelling
  • Numerical modelling of coastal and hydraulic engineering
  • Air pollution and urban flow
  • Solid and fluid coupling
  • Multi-phase flows
  • Environmental problems, e.g. sediment, numerical modelling of water quality and oil spills
  • Reservoir modelling


  • April, 2010---Now, Research Fellow in Earth Science and Engineering, Imperial College, London, UK.
  • 2011-2014, Visiting Professor at Wuhan University, China.
  • 2009---2010, Research Fellow/Scientist attached to both Imperial College London and the University of Karlsruhe Germany.
  • May, 2002---2009, Research associate in Earth Science and Engineering, Imperial College, London, UK.
  • Oct., 2000---Jan. 2002, Postdoctoral researcher in Laboratorie des Etudes Géophysiques et Océanographiques Spatiales, CNES/CNRS/OMP, Toulouse, France.
  • Feb.,1995---Aug. 1999, PhD candidate, Department of Civil & Environmental Engineering, James Cook University, Australia.
  • July,1987---Feb. 1995, Lecturer, North China Institute of Water Conservancy and Hydropower, Beijing, China.
  • July, 1984---July,1987, Research Assistant, Institute of Water conservancy and Hydroelectric Power Research, China

Other Significant Activities

  • Executive manager of data assimilation laboratory, at Data Science Institute of Imperial College London; 
  • Member of international expert group at Institute of Atmospheric and Physics, CAS, China;
  • Lead the collaboration with collaborations with Chinese Universities;
  • Organised short course on reduced order modelling and data assimilation;
  • Organised workshop and training courses on computational dynamics code (Fluidity), reduced order modelling and data assimilation in China and UK;
  • Supervision of PhD students working on inversion problems.
  • Was one of two external examiners in the PhD jury at Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland;
  • Was invited as a guest scientist at EPFL, Lausanne.
  • A member on the editorial board for the Open Oceanography Journal;
  • Manage the adjoint codes and test cases for other people to use;

Other publications

  • F. Fang, C. C. Pain, I. M. Navon, M. D. Piggott, G. J. Gorman, P. Allison, A. J. H. Goddard. A goal-orientated mesh adaptive and dual-weighted POD for data inverse problems. In preparation, 2009.

Selected Publications

Journal Articles

Cheng M, Fang F, Pain CC, et al., 2020, An advanced hybrid deep adversarial autoencoder for parameterized nonlinear fluid flow modelling, Computer Methods in Applied Mechanics and Engineering, Vol:372, ISSN:0045-7825, Pages:1-19

Cheng M, Fang F, Kinouchi T, et al., 2020, Long lead-time daily and monthly streamflow forecasting using machine learning methods, Journal of Hydrology, Vol:590, ISSN:0022-1694, Pages:1-13

Cheng M, Fang F, Pain CC, et al., 2020, Data -driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network, Computer Methods in Applied Mechanics and Engineering, Vol:365, ISSN:0045-7825

More Publications