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

In Memoriam Piet Hemker

In Memoriam Piet Hemker

With sadness we announce that CWI Fellow and former CWI researcher Piet Hemker passed away on 27 May. Hemker had been working at CWI from 1970–2006 and since 1989 also as a professor at the UvA. He has been a CWI Fellow since 2001 and was named Knight in the Order of the Netherlands Lion in 2006.

In Memoriam Piet Hemker - 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