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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

Supervision

Past postdocs, PhD students etc. in italic

Postdocs

(In collaboration with Enrico Camporeale)

PhD students

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

  • Hemaditya Malla
  • Dennis Bouwman
  • Andy Martinez
  • Baohong Guo
  • Xiaoran Li
  • Zhen Wang

PhD commitee member

  • Alejandro Malagon (University of Granada)
  • Shahriar Mirpour (TU Eindhoven)
  • Andy Martinez (TU Eindhoven)
  • Brecht Laperre (KU Leuven)

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 (small) projects

A (very!) incomplete list of research ideas, some of which are suitable for student projects:

  • Using machine learning to create a heuristic simple model from a more complex one (for example going from a particle-in-cell to a fluid model)
  • 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
  • Coupling explicit and implicit time integration for plasma fluid models
  • Coupling stiff chemistry to simulations with AMR (adaptive mesh refinement), where the chemistry can be evaluated at coarser resolution and perhaps partially implicitly.
  • 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
  • Using DSMC (or similar particle-based simulations) to evaluate (and improve?) the behavior of continuum hydrodynamics schemes at low pressures/densities
  • Exploring methods to make conventional hydrodynamics schemes more robust (i.e., avoiding negative pressures)

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