Efficient multifidelity data-informed models for urban air quality

A fully-funded four year EPSRC Industrial Case PhD studentship is available, starting on or after 1st October 2020. The research will be undertaken within the Numerical Analysis group in the Department of Mathematics and Statistics at the University of Strathclyde, supervised by Professor John Mackenzie and Dr Alison Ramage. Please see http://www.mathstat.strath.ac.uk/research/groups/nasc for details of the group’s research. The iCase partner is the National Physical Laboratory (NPL), the UK’s National Measurement Institute, and the industrial supervisor will be Professor Alastair Forbes.

The student will be part of a project which addresses the challenge of modelling one of the most important environmental problems currently affecting people’s health, urban air pollution. Although it is well established that urban air quality can be modelled mathematically using partial differential equations, the inclusion of uncertainty propagation for this class of models requires multiple model evaluations with many different inputs, leading to excessive computational demands if only a high-fidelity model is used. There is therefore a pressing need for models which combine such techniques with reduced order approaches and parameter estimation informed by observational data sets.

The aim of this project is to develop such multifidelity methods to accelerate the solution of uncertainty propagation by combining techniques from mathematical modelling, statistics, linear algebra and data science. The project will place the student at the forefront of research in numerical methods, and provide an excellent opportunity to develop skills working at the interface between applied mathematics, engineering and industry.

Applicants should have, or be expecting to obtain in the near future, a first class or good 2.1 honours degree (or equivalent) in mathematics, engineering or a mathematical science. The student
will spend some time working at NPL in Teddington, Middlesex during the period of the
award.