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With ever increasing computational power it has become possible to solve physical problems of an unprecedented complexity. I am interested in numerical techniques for simulating systems at all length scales, from high resolution atmospheric models to subatomic particles. As a member of the Numerical Analysis group I work on the development of fast numerical algorithms and their implementation in efficient and parallel computer code.
In the past I contributed to several areas of Scientific Computing, most recently as a PostDoc (University of Bath, Sep 2011 - Jan 2015), where I developed and improved massively parallel solvers for the pressure correction in numerical weather- and climate forecast models; this work was carried out in collaboration with the Met Office as part of the GungHo! project. Prior to this I worked as a research scientist at the Met Office (Nov 2009 - Aug 2011). My main task was the improvement and OpenMP parallelisation of the NAME model code for the prediction of the transport of atmospheric pollutants, such as volcanic ash. I am a physicist by training and received a PhD in computational particle physics from the University of Edinburgh (Nov 2009), after studying in Germany and Scotland.
The focus of my current research is the development of fast solvers for PDEs, with particular focus on writing efficient, yet maintainable, code in suitable frameworks such as DUNE and firedrake/PyOP2. Together with external partners I apply those techniques to real-life problems such as numerical weather- and climate forecast models. In addition to implementations on massively parallel CPU clusters I am interested in modern manycore architectures (GPUs and Xeon Phis). I also work on the application of Multilevel Monte Carlo methods to atmospheric dispersion modelling and the development of a performance portable framework for atomistic simulations in physics and chemistry.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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- 2 Finished
Mueller, E. & Griffith, M.
1/12/21 → 31/03/22
Project: Central government, health and local authorities
Mueller, E. & Griffith, M.
1/06/21 → 30/06/22
Project: Research council
Deep surrogate accelerated delayed-acceptance HMC for Bayesian inference of spatio-temporal heat fluxes in rotating disc systemsDeveney, T., Mueller, E. & Shardlow, T., 5 Apr 2022.
Research output: Working paper / Preprint › PreprintFile8 Downloads (Pure)
Saunders, W., Grant, J. & Müller, E., 1 Apr 2021, In: Journal of Computational Physics. 430, 22 p., 110099.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile35 Downloads (Pure)
Multigrid preconditioners for the hybridized Discontinuous Galerkin discretisation of the shallow water equationsBetteridge, J., Gibson, T., Graham, I. & Müller, E., 1 Feb 2021, In: Journal of Computational Physics. 426, 42 p., 109948.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile2 Downloads (Pure)
Saunders, W., Grant, J., Müller, E. & Thompson, I., 1 Jun 2020, In: Journal of Computational Physics. 410, 28 p., 109379.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile5 Citations (SciVal)35 Downloads (Pure)
Multigrid preconditioners for the mixed finite element dynamical core of the LFRic atmospheric modelMaynard, C., Melvin, T. & Müller, E., 31 Oct 2020, In: Quarterly Journal of the Royal Meteorological Society. 146, 733, p. 3917-3936 13 p.
Research output: Contribution to journal › Article › peer-reviewOpen Access4 Citations (SciVal)