Description

Leader of the group Scientific Computing: Daan Crommelin.

The SC groups 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, finance and biology. 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, data assimilation, stochastic multiscale modeling and risk assessment. 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.

 

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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…

Members

Associated Members

Publications

Current projects with external funding

  • Towards cloud-resolving climate simulations
  • Valuation Adjustments for Improved Risk Management (ABC-EU-XVA)
  • Physics based ICT: The digital twin in pipelines (DP-Trans)
  • Dronesurance
  • Sloshing of Liquefied Natural Gas: subproject Variability (14-10-project2) (SLING)
  • Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
  • Verified Exascale Computing for Multiscale Applications (VECMA)
  • WIND Turbine Rotor aeroelasticity UncErtainty quantification (WINDTRUE)

Related partners

  • DNV GL Netherlands B.V
  • Max Planck Institute for Informatics
  • Bull Sas
  • CBK Sci Con Ltd
  • Bayerische Akademie der Wissenschaften
  • Maritiem Research Instituut
  • MeitY
  • Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
  • Rijksuniversiteit Groningen
  • Suzlon Blades Technology
  • TNO
  • Technische Universiteit Eindhoven
  • Technische Universiteit Delft
  • Brunel University London
  • University College London
  • Universiteit Leiden
  • Universiteit van Amsterdam