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

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.

• Plasma for Plants (TTW)
• Let CO2 spark! (TTW)
• Plasma assisted combustion (TTW)
• AIDA (H2020)
• ESCAPE (H2020)

These are some of the simulation codes that I have developed or worked on:

• MPI-AMRVAC
• Afivo
• Afivo-streamer
• Octree-mg
• Particle_swarm
• pamdi3d
• streamer_1d

And these are some of the (simulation) utilities that I have developed:

• config_fortran
• lookup_table_fortran
• rng_fortran
• morton_fortran

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

• 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
• Adding support for internal boundary conditions in a geometric multigrid solver.
• Exploring efficient methods for solving the coarse grid equations in a geometric multigrid method.
• Enabling efficient visualization of octree AMR data in Visit or Paraview.
• 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.
• Investigating the so-called “stability field” of streamer discharges through computations, with the goal of predicting how this field depends on the gas.
• Extending the discharge model comparison of this paper to other fluid models
• Compare particle-in-cell and plasma fluid models for 2D and 3D simulations of streamer discharges.
• 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)