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Jannis Teunissen


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Research

Experience and interests

My general research interests are scientific computing, computational (plasma) physics and more recently also machine learning.

I have developed both particle-in-cell and plasma fluid codes for the simulation of electric discharges (non-thermal plasmas). I'm also active in the development of MPI-AMRVAC, a framework for (magneto)hydrodynamics simulations. My work has focused on topics such as adaptive mesh refinement (AMR) and fast elliptic solvers (e.g., multigrid). I like to study systems that have some intrinsic complexity, not coming from boundary conditions or input data.

Since 2018, I have been working on machine learning methods applied to space weather applications, in collaboration with Enrico Camporeale, as I have taken over two EU projects in this direction from Enrico (AIDA and ESCAPE, see below). Current research focuses on forecasting time-series data, recognizing magnetic reconnection, and the use of unsupervised methods for e.g. dimensionality reduction and clustering.

Research projects

  • 2024-2026 AGILE: Astrophysics on GPUs for InterdiscipLinary Exascience challenges
    Collaboration with O. Porth (PI, UvA) and others, funded by Netherlands eScience Center.
    Total budget: 228 k€, mostly for paying eScience staff working on the project.
  • 2023-2029 REGENERATE: Reliable Next Generation Actuation Systems
    NWO KIC project, collaboration with TU Eindhoven, TU Delft and industry partners.
    Total budget: 3.36 M€. CWI got one PhD position.
  • 2024-2028 Green Sparks
    TTW OTP project #20344, in collaboration with TU Eindhoven and industry partners.
    Total budget: 1.25 M€. CWI got a PhD position and an 20 month postdoc position.
  • 2019-2024 Plasma for Plants
    TTW OTP project #17183, in collaboration with TU Eindhoven and industry partners.
    Total budget: 775 k€. CWI got an 24 month postdoc position.
  • 2018-2022 Plasma assisted combustion
    TTW OTP project #16480 in collaboration with TU Eindhoven and industry partners
    Total budget: 903 k€. CWI got 1.5 postdoc years.
  • 2017-2022 Let CO2 spark!
    TTW OTP project #15052, in collaboration with TU Eindhoven and industry partners.
    Total budget: 975 k€. CWI got funding for one PhD position and a 24 month postdoc position.
  • 2018-2022 AIDA
    EU H2020 project #776262, in collaboration with KU Leuven and other partners.
    Total budget: 1.5 M€. CWI got 219 k€.
  • 2019-2023 ESCAPE
    EU H2020 project #824064, large collaboration between European research organizations.
    Total budget: 16.0 M€. CWI got a 24 months postdoc position.

Supervision

Past postdocs, PhD students etc. in italic

Postdocs

PhD students

I (co-)supervise(d) the following PhD students together with Ute Ebert:

  • Yuting Gao (daily supervisor)
  • Thom Smits
  • Hemaditya Malla (daily supervisor)
  • Dennis Bouwman
  • Baohong Guo (daily supervisor)
  • Xiaoran Li (daily supervisor)
  • Zhen Wang (daily supervisor)
  • Andy Martinez

PhD commitee member

  • Yihao Guo (2025, TU Eindhoven)
  • Hemaditya Malla (2024, TU Eindhoven)
  • Baohong Guo (2023, TU Eindhoven)
  • Zhen Wang (2023, TU Eindhoven)
  • Dennis Bouwman (2023, TU Eindhoven)
  • Xiaoran Li (2023, TU Eindhoven)
  • Hani Francisco (2023, TU Eindhoven)
  • Brecht Laperre (2022, KU Leuven)
  • Andy Martinez (2022, TU Eindhoven)
  • Alejandro Malagon (2021, University of Granada)
  • Shahriar Mirpour (2021, TU Eindhoven, co-promotor)

MSc / BSc students

  • Francesca Schiavello (2021, UvA MSc)
  • Chris van der Heijden (2021, TU/e BSc)
  • Stijn van Deutekom (2020, TU/e BSc)

Ideas for research projects

Examples of research/project ideas:

  • Simulating incompressible flow with the Afivo framework
  • Exploring hybrid OpenMP/MPI parallelization for AMR frameworks
  • Solving plasma fluid equations implicitly. In particular, what is a good preconditioner?
  • Improving the convergence rate of Monte Carlo particle swarm simulations in low electric fields. One idea is to limit the drift in electron momentum due to collisions.
  • Coupling explicit and implicit time integration for plasma fluid models
  • Performing large scale 3D simulations of sprite formation
  • Coupling particle and fluid models in energy space, for the study of runaway electron production in electric discharges.
  • Extending the discharge model comparison of this paper to other fluid models