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.
These are some of the simulation codes that I have developed or worked on:
|MPI-AMRVAC doc||Parallel AMR framework aimed at hyperbolic PDEs, with a focus on (magneto)hydrodynamics|
|Afivo doc||Parallel AMR framework with multigrid methods|
|Afivo-streamer doc||Parallel AMR code for streamer discharge simulations|
|Octree-mg||MPI-parallel geometric multigrid library, AMR compatible|
|Particle_swarm||Monte Carlo Boltzmann solver using electron swarm|
|pamdi3d||Particle-in-cell discharge simulation code|
|streamer_1d||1D particle and fluid code for discharge simulations|
And these are some of the (simulation) utilities that I have developed:
A (very!) incomplete list of research ideas, some of which are suitable for student projects: