Projects per year
Personal profile
Research interests
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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Collaborations and top research areas from the last five years
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MGHyPE: An ExaHyPE version with a multigrid solver
Engineering and Physical Sciences Research Council
1/12/22 → 30/11/24
Project: Research council
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Research and development of a multigrid preconditioner for the LFRic hybridized solver
Mueller, E. & Griffith, M.
1/12/21 → 31/03/22
Project: Central government, health and local authorities
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IAA – Accelerating climate- and weather-forecasts with faster multigrid solvers
Mueller, E. & Griffith, M.
Engineering and Physical Sciences Research Council
1/06/21 → 30/06/22
Project: Research council
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Research output
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Exact conservation laws for neural network integrators of dynamical systems
Müller, E., 1 Sept 2023, In: Journal of Computational Physics. 488, 24 p., 112234.Research output: Contribution to journal › Article › peer-review
Open Access -
Deep surrogate accelerated delayed-acceptance HMC for Bayesian inference of spatio-temporal heat fluxes in rotating disc systems
Deveney, T., Mueller, E. & Shardlow, T., 5 Apr 2022.Research output: Working paper / Preprint › Preprint
Open AccessFile26 Downloads (Pure) -
A new algorithm for electrostatic interactions in Monte Carlo simulations of charged particles
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-review
Open AccessFile1 Citation (SciVal)50 Downloads (Pure) -
Multigrid preconditioners for the hybridized Discontinuous Galerkin discretisation of the shallow water equations
Betteridge, 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-review
Open AccessFile3 Citations (SciVal)8 Downloads (Pure) -
Fast electrostatic solvers for kinetic Monte Carlo simulations
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-review
Open AccessFile6 Citations (SciVal)49 Downloads (Pure)