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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

(Past projects in italics)

Supervision

Past postdocs, PhD students etc. in italic

Postdocs

(In collaboration with Enrico Camporeale)

PhD students

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

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

PhD commitee member

  • 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)

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

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