Efficient multifidelity data-informed models for urban air quality

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.