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.

(Past projects in italics)

• REGENERATE: Reliable Next Generation Actuation Systems (NWO KIC project)
• Plasma for Plants (TTW) (#17183)
• Plasma assisted combustion (TTW) (#16480)

• Let CO2 spark! (TTW) (#15052)
• AIDA (H2020 - 776262) EU info
• ESCAPE (H2020 - 824064) EU info

Past postdocs, PhD students etc. in italic

• Ajay Tiwari
• Andong Hu

(In collaboration with Enrico Camporeale)

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

• 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)

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

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