Description

Leader of the group Scientific Computing: Benjamin Sanderse.

The Scientific Computing group at CWI develops efficient mathematical methods to simulate and predict real-world phenomena with inherent uncertainties. Such uncertainties arise from e.g. uncertain model parameters, chaotic dynamics or intrinsic randomness, and can have major impact on model outputs and predictions. Our work is targeted in particular at applications in climate, energy, and finance. In these vital areas, the ability to assess uncertainties and their impact on model predictions is of paramount importance. Expertise in the SC group includes uncertainty quantification, reduced order modeling, data assimilation, and stochastic multiscale modeling. The availability of data to inform and improve simulations and predictions, for example through learning and data-driven modeling, plays an important role in our research.

 

 

The SC group organizes the seminar on Machine Learning and Uncertainty Quantification in Scientific Computing.

 

 

 

 

 

Vacancies

No vacancies currently.

News

Cells ‘walk’ to firm ground

Cells ‘walk’ to firm ground

A new mathematical model may explain how body cells get their shapes and what makes them move within a tissue. The model provides fundamental knowledge for applications in tissue engineering, amongst other things. The research was executed by Roeland Merks and Lisanne Rens, who were previously affiliated with CWI's Scientific Computing group.

Cells ‘walk’ to firm ground - Read More…

Members

Associated Members

Publications

Current projects with external funding

  • Physics based ICT: The digital twin in pipelines (DP-Trans)
  • Learning small closure models for large multiscale problems. (None)
  • Robust numerical modelling for transient multiphase CO2 transport (SHELL)
  • Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
  • Discretize first, reduce next: a new paradigm to closure for fluid flow simulation (Vidi Sanderse)

Related partners

  • Shell, Amsterdam
  • MeitY
  • Technische Universiteit Eindhoven
  • Universiteit Leiden