The Scientific Computing Group at CWI
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 upcoming semester programme 2025 on Synergies in numerical linear algebra and machine learnin!
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), Syver Agdestein (PhD candidate), Toby van Gastelen (PhD candidate), Rik Hoekstra (PhD candidate), Pardeep Kumar (PhD candidate), Marius Kurz (postdoc), Nikolaj Mücke (postdoc), Henrik Rosenberger (PhD candidate), Robin Klein (PhD candidate), Barry Koren (advisor). News: Dimitris Loukrezis will join our group as a tenure track researcher, starting March 1st, 2025. Welcome Dimitris!
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.
- 2024-11-19Seminar Jelmer Wolterink
- Exploiting Symmetries for Personalized Hemodynamics Modeling in Cardiovascular Disease
- 2024-09-12Seminar Dimitris Loukrezis
- Scientific Machine Learning and Uncertainty Quantification for Predictive Digital Twins
- 2024-09-06Seminar Michael Abdelmalik
- Neural Green's Operators for Parametric Partial Differential Equations
- 2024-06-20Seminar Francesca Bartolucci
- Representation Equivalent Neural Operators: A Framework for Alias-free Operator Learning
- 2024-05-27Seminar Beatriz Moya
- Exploring the role of geometric and learning biases in Model Order Reduction and Data-Driven simulation
News
- 2024-07-26AINed XS funding
- AINed XS funding for generative AI
- 2024-06-19Semester programme 2024
- Semester programme on uncertainty quantification 2024
Publications
Full list of publications at CWI's institutional repository
- 2025-03-01Scientific Machine Learning: A Symbiosis
- Keith, O'Leary-Roseberry, Sanderse, Scheichl, van Bloemen Waanders, Scientific Machine Learning: A Symbiosis
- 2025-03-01Scientific machine learning for closure models in multiscale problems: A review
- Sanderse, Stinis, Maulik, Ahmed, Scientific machine learning for closure models in multiscale problems: A review
- 2024-07-01Energy-Conserving Neural Network for Turbulence Closure Modeling
- Toby van Gastelen, Wouter Edeling, Benjamin Sanderse, in Journal of Computational Physics
- 2024-03-26Discretize first, filter next: learning divergence-consistent closure models for large-eddy simulation
- Syver Døving Agdestein, Benjamin Sanderse, arXiv preprint arXiv:2403.18088
- 2022-11-24Discretize first, filter next – a new closure model approach
- Syver Døving Agdestein, Benjamin Sanderse, in Eccomas 2022