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Towards exascale simulations of space plasmas using the DEEP-EST architecture.


Space Weather studies the effects of solar activity on human life and technology.

The Sun is an active star. It constantly releases plasma meterial into space, sometimes during explosive events. This plasma travels trough space and reaches our planet. When it interacts with our atmosphere and the natural magnetic field of the planet, it can cause communication disruptions, GPS inaccuracies, overloading of the electric grid, and radiation of astronauts and high-altitude airplane passengers.


KULeuven has developed a first principle approach implemented in the code xPic and based on modelling a statistical sample of electrons and ions and the electromagnetic fields of the space environment. xPic is chiefly suitable for the DEEP/-ER/-EST Architecture, where the highly scalable and highly vectorisable particle operations run in the Booster while the more communication intensive field operations run concurrently on the Cluster.


Use of DEEP-EST technologies for plasma simulations

The relative conceptual simplicity of the key parts of the xPic algorithms enable synergistic co-design opportunities where modifications of the algorithm can be easily implemented to test innovative and even radical resilience solutions. Currently both I/O and checkpointing are handled at the application level using HDF5 files saved on the disk by each core at cycle intervals set by the user. The xPic application tests the new approaches defined in DEEP-ER in a peculiarly demanding limit.

The code xPic uses many of the new technologies and software developed in the DEEP-EST project, including intra-module communications, parallel I/O, and continuous testing.

Machine Learning for Space Weather

The Sun is located 150 million kilometers away from the Earth. Simliar to weather stations, spacecraft are located in space to collect information about the solar wind to improve our computer predictions. However, due to the forces of gravity, we can only station satellites 1.5 million kilometers ahead of the Earth, 1/100th the distance to the Sun.

Instead of using simulations, KU Leuven uses Machine Learning techniques to forecast the solar wind activity in front of the Earth. KU Leuven uses Terabytes of data from satellites, including solar images and in-situ measurements of the solar wind, to forecast future conditions of the near Earth environment. This information is also used to create initial conditions for the code xPic.