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Computational and Industrial Mathematics

Unit(s) of assessment: General Engineering

School: School of Science and Technology


Design and construction decisions are increasingly made by means of virtual prototyping and as part of Computer Aided Engineering. Efficient simulation tools in all areas of engineering are prevalent and sought-after. The increasing availability of computational resources since the mid-twentieth century has nurtured the growth of a simulation industry, serving the needs of end-users in the manufacturing sector. At the heart of these simulation methods are mathematical models, which have traditionally been physics-based models using differential equations. In more recent times, data-driven approaches using machine learning have also enjoyed widespread success.

The Computational and Industrial Mathematics research group develops bespoke mathematical and numerical models for problems arising in both engineering and the natural sciences, starting from the mathematical model at the core of the problem. Based within the Mathematical Sciences team and forming part of the Department of Physics and Mathematics, the group’s expertise spans both physics-based and data-driven models.


Academic collaborators

  • Columbia University
  • Heriot-Watt University
  • Lawrence Berkeley National Laboratory
  • Loughborough University
  • Queen Mary University of London
  • University of Brighton
  • University of Leicester
  • University of Nottingham
  • University of Southampton

Industrial collaborators:

  • Bowers and Wilkins Ltd
  • CDH AG
  • inuTech Gmbh
  • Jaguar Land Rover Ltd
  • Far UK Ltd
  • PACYS Ltd


Fixed-term contract staff:

  • David Jenkins (Academic Associate)
  • Tuan Bohoran (MSCA Research Fellow)


  1. CHAPPELL, D., CROFTS, J.J., RICHTER, M. and TANNER, G., 2021. A direction preserving discretization for computing phase-space densities. SIAM Journal on Scientific Computing, 43(4), B884-B906.
  2. CHAPPELL, D.J. and O'DEA, R.D., 2020. Numerical-asymptotic models for the manipulation of viscous films via dielectrophoresis. Journal of Fluid Mechanics, 901: A35.
  3. EGAN, C.P., BOURNE, D.P., COTTER, C. J., CULLEN, M. J. P., PELLONI, P., ROPER, S. M. and WILKINSON, M., 2022. A New Implementation of the Geometric Method for Solving the Eady Slice Equations. Journal of Computational Physics, 469:111542.
  4. KHUSNUTDINOVA, K.R. and TRANTER, M.R., 2017. On radiating solitary waves in bi-layers with delamination and coupled Ostrovsky equations. Chaos, 27(1):013112.
  5. GIANNAKIDIS, A., MELKUS, G., YANG, G. and GULLBERG, G. T., 2016. On the averaging of cardiac diffusion tensor MRI data: The effect of distance function selection. Physics in Medicine and Biology 61(21):7765--7786.
  6. SIOURAS, A., MOUSTAKIDIS, S., GIANNAKIDIS, A., CHALATSIS, G., LIAMPAS, I., VLYCHOU, M., HANTES, M., TASOULIS, S. and TSAOPOULOS, D., 2022. Knee injury detection using deep learning on MRI studies: A systematic review. Diagnostics 12(2):537.
  7. TAMBER, J.S. and TRANTER, M.R., 2022. Scattering of an Ostrovsky wave packet in a delaminated waveguide. Wave Motion, 114:103023.
  8. WILKINSON, M., 2022. A Lie Algebra-theoretic Approach to Characterisation of Collision Invariants of the Boltzmann Equation for General Convex Particles, Kinetic and Related Models, 15(2):283-315.