Nature and technology are full of dynamics, often involving multiple scales in length, time and energy. To model these processes, we combine scientific computing with model reduction and machine learning, with particular focus on plasma dynamics in lightning and space weather, and in high voltage and plasma technology.

Our research addresses questions in nature such as start and propagation of lightning strokes, terrestrial gamma-ray flashes and space weather, and closely related technological problems such as switch gear for long-distance electricity nets, air purification and disinfection with corona reactors, and protection of satellites and electricity nets from space weather.

Within national and European projects, we collaborate with colleagues in applied plasma physics, electrical and mechanical engineering, atmospheric electricity, and cosmic particle and space science, and with non-academic partners such as ABB, DNV-GL, ESA-ESTEC and NASA.

Find more about our work (including publications) on the personal homepages of the staff scientists:

and on the page with our numerical codes for Multiscale Plasma Dynamics.

View a photo of the Multiscale Dynamics group in June 2022January 2020 and in January 2018.




No vacancies currently.


Current events

Multiscale Dynamics Seminar Ajay Tiwari

  • 2022-07-14T14:00:00+02:00
  • 2022-07-14T15:00:00+02:00
July 14 Thursday

Start: 2022-07-14 14:00:00+02:00 End: 2022-07-14 15:00:00+02:00


Title: Machine Learning in Space Weather

Abstract: The ever increasing popularity and recent advancements in machine learning (ML) techniques have led to a variety of new tools for resolving traditional and emerging challenging problems more powerfully from data-driven perspectives. There have been various attempts to catalogue Coronal Mass Ejections (CME) observations, in this work we benchmark four of the more popular CME databases for CME time-of-arrival prediction using machine learning methods. We identify the important features in the various databases and quantify the performance of machine learning methods. To this end we have also created an interactive CME ML playground where the databases can be accessed and trained on the cloud.


Associated Members



Current projects with external funding

  • Making plasma-assisted combustion efficient ()
  • European Science Cluster of Astronomy & Particle physics ESFRI research infrastructures (ESCAPE)
  • Let CO2 spark! Understand breakdown dynamics for high voltage technology and lightning Abstract Sparks, (Let CO2 spark!)
  • Plasma for Plants: Towards controlled and efficient plasma-activated water generation for a cleaner environment (Plasma for Plants)

Related partners

  • Technische Universiteit Eindhoven