At the cross-road of the energy and digital revolutions, EoCoE develops and applies cutting-edge computational methods in its mission to accelerate the transition to the production, storage and management of clean, decarbonized energy.
EoCoE is anchored in the High Performance Computing (HPC) community and targets research institutes, key commercial players and SMEs who develop and enable energy-relevant numerical models to be run on exascale supercomputers, demonstrating their benefits for low-carbon energy technology.
EoCoE is build upon a world-class consortium of 18 complementary partners from 7 countries that forms a unique network of expertise in energy science, scientific computing and HPC, including 3 leading European supercomputing centers.
EoCoE drives its efforts into 5 scientific Exascale challenges in the low-carbon sectors of energy: Meteorology, Materials, Water, Wind and Fusion. This multidisciplinary effort will harness innovations in computer science and mathematical algorithms within a tightly integrated co-design approach to overcome performance bottlenecks and to anticipate future HPC hardware developments.
Challenging applications in selected energy sectors will be created at unprecedented scale, demonstrating the potential benefits to the energy industry, such as accelerated design of storage devices, high-resolution probabilistic wind and solar forecasting for the power grid and quantitative understanding of plasma core-edge interactions in ITER-scale tokamaks.
By design, each of these Challenges exhibits a complexity level which requires exascale computing resources for its solution, while immediately posing a set of technical challenges for the numerical modelling, algorithmic kernels and computer science methodology from which these applications are constructed. Thus new methods and codes are needed to rebuilt from scratch or deeply refactoring numerical approaches within an integrated co-design strategy to cope successfully forthcoming hardware architectures, and be able to mitigate the increase of run-time errors associated with the extreme parallelism. At the same time, they will need to remain relevant to their communities in order to maximize their impact, attract interest from users outside the consortium and the engagement of energy industrials and SMEs.