Scientific Computing

The Scientific Computing group at CWI develops efficient mathematical methods to simulate and predict real-world phenomena with inherent uncertainties. Our two main research themes are scientific machine learning and uncertainty quantification, and the topics within these themes are neural ODEs, closure models for turbulence, reduced-order models, discretization techniques, stochastic parameterizations, generative models, data assimilation.

Our official CWI page with general information can be found here. On the current page, you can find more detailed information such as the software that we develop in the group and other useful information such as the material used in the semester programmes that we organize, in particular the SciML and UQ semester programmes. Note the recent semester programme 2025 on Synergies in numerical linear algebra and machine learning!

Please check our GitHub page with group repositories and our software page.

Members ​

Current members:
Benjamin Sanderse (group leader),
Wouter Edeling (staff),
Daan Crommelin (staff),
Dimitris Loukrezis (staff),
Syver Agdestein (PhD candidate),
Toby van Gastelen (PhD candidate),
Rik Hoekstra (PhD candidate),
Pardeep Kumar (PhD candidate),
Nikolaj Mücke (visiting postdoc),
Henrik Rosenberger (PhD candidate),
Robin Klein (PhD candidate),
Barry Koren (advisor),
Bernard Geurts (visiting professor).

Group seminar ​

To receive news and Zoom links for our group seminar, you can contact Wouter Edeling at wouter.edeling@cwi.nl. For more information, see our seminar page.

Group challenges ​

Our group is regularly involved in group challenges, in which we, as a group, tackle an outstanding societal challenge that is given to us by a research institute or industry. These are short-term projects, typically 3-6 months in duration, on which we work in a team of approximately 5 people on Friday afternoons. We add value through our extensive knowledge of scientific computing, uncertainty quantification and scientific machine learning, and our strong programming skills in modern languages such as Python and Julia. Over the last year, we worked with Deltares on predicting failure probabilities of Dutch dikes, and with KNMI on the fine-grained temperature prediction in Europe using Gaussian process regression.

News ​

• Semester programme 2025
  31-10-2025
• ENW XS funding
  08-05-2025
• AINed XS funding
  26-07-2024
• Semester programme 2024
  19-06-2024

Publications ​

Full list of publications can be found at CWI's institutional repository.

• A new data-driven energy-stable Evolve-Filter-Relax model for turbulent flow simulation
  23-07-2025
• Exact closure for discrete large-eddy simulation
  22-07-2025
• Exact operator inference with minimal data
  26-06-2025
• Energy-Conserving Neural Network Closure Model for Long-Time Accurate and Stable LES
  08-04-2025
• Scientific Machine Learning: A Symbiosis
  01-03-2025
• Scientific machine learning for closure models in multiscale problems: A review
  01-03-2025
• Discretize first, filter next: learning divergence-consistent closure models for large-eddy simulation
  01-02-2025
• Reduced Data-Driven Turbulence Closure for Capturing Long-Term Statistics
  15-12-2024
• Modeling Advection-Dominated Flows with Space-Local Reduced-Order Models
  13-09-2024
• Energy-Conserving Neural Network for Turbulence Closure Modeling
  01-07-2024
• Discretize first, filter next – a new closure model approach
  24-11-2022

Former members ​

Anna Ivagnes (at SISSA)
Josep Plana-Riu (at UPC)
Marius Kurz (now at AMD)
Kelbij Start (now at Deltares)
Laurent van den Bos (now at ASML)
Yous van Halder (now at Univé)
Anne Eggels
Svetlana Dubinkina (now at VU)
Kees Oosterlee (now at Utrecht University)
Barry Koren (retired from Eindhoven University)
Prashant Kumar
Sangeetika Ruchi
Bart de Leeuw
Jesse Dorrestijn
Keith Meyerscough