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

Reality check for financial risk assessment tool concludes CWI-led project

Reality check for financial risk assessment tool concludes CWI-led project

One of the most promising tools for assessing financial risks proves to hold up after a thorough mathematical gauging. CWI researcher Ki Wai Chau developed a numerical analysis of complex algorithms that are developed to support financial risk management. His method provides a reality check for those algorithms, paving the way for future applications.

Reality check for financial risk assessment tool concludes CWI-led project - Read More…

Smart mobility start-up Skialabs launched by CWI researchers

Smart mobility start-up Skialabs launched by CWI researchers

Traffic flows in cities could be managed much more efficiently thanks to cutting-edge technology pioneered by the new start-up Skialabs. By using huge dataflows collected in cities, the Skialabs platform provides a real-time view on the mobility flows in the city. This allows creating cost-effective and sustainable mobility services that react instantly to the demands of the end-users.

Smart mobility start-up Skialabs launched by CWI researchers - Read More…

Extreme events better investigated with new math method

Extreme events better investigated with new math method

Fortunately, incidents like extreme weather, earthquakes or massive power grid blackouts are a rare occurrence. Analysing properly how likely such rare incidents are to happen can be very valuable, but also very challenging. In his PhD thesis, Krzysztof Bisewski developed mathematical methods that greatly speed up simulations for estimating the probability of rare events.

Extreme events better investigated with new math method - Read More…

New data framework illuminates uncertainties in offshore wind farm conditions

New data framework illuminates uncertainties in offshore wind farm conditions

Wind speeds and wave heights can have a major effect on offshore wind farms. But because they are correlated, their combined significance for wind farm designs couldn’t be factored in until now. CWI researcher Anne Eggels developed methods which take the effect of such correlations or dependencies into account. Today, she will publicly defend her thesis at the University of Amsterdam.

New data framework illuminates uncertainties in offshore wind farm conditions - 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