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

Better estimation of financial risks possible with maths

Better estimation of financial risks possible with maths

Due to the recent financial crisis, the requirements imposed on banks have been made stricter. Banks must model the credit risk of the counterparties now in their portfolios, for instance. A measure for this is the credit value adjustment (CVA): the difference between the value of a portfolio without credit risk and the value if a possible bankruptcy of the counterparty is included. Qian Feng modelled CVAs and designed a new algorithm that can help banks estimate the risks precisely, so they can take appropriate measures if necessary.

Better estimation of financial risks possible with maths - Read More…

The Netherlands’ smallest supercomputer is here

The Netherlands’ smallest supercomputer is here

A team of Dutch scientists has built a supercomputer the size of four pizza boxes. The Little Green Machine II has the computing power of 10,000 PCs and will be used by researchers in oceanography, computer science, artificial intelligence, financial modeling and astronomy. CWI researchers Joost Batenburg and Kees Oosterlee, who were part of the development team, will use the machine for computational imaging and machine learning for time series respectively. The computer is based at Leiden University (the Netherlands) and developed with help from IBM.

The Netherlands’ smallest supercomputer is here - Read More…

Members

Associated Members

Publications

Current projects with external funding

  • Accurate prediction of slugs in multiphase pipe flow simulation for improved oil and gas production
  • Towards cloud-resolving climate simulations
  • Valuation Adjustments for Improved Risk Management (ABC-EU-XVA)
  • Physics based ICT: The digital twin in pipelines (DP-Trans)
  • Excellence in Uncertainty Reduction of Offshore Wind Systems (EUROS)
  • Rare Event Simulation for Climate Extremes (RESClim)
  • Sloshing of Liquefied Natural Gas: subproject Variability (14-10-project2) (SLING)
  • Verified Exascale Computing for Multiscale Applications (VECMA)
  • WIND Turbine Rotor aeroelasticity UncErtainty quantification (WINDTRUE)

Related partners

  • DNV GL Netherlands B.V
  • FOM
  • Max Planck Institute for Informatics
  • Shell, Amsterdam
  • Bull Sas
  • CBK Sci Con Ltd
  • Bayerische Akademie der Wissenschaften
  • 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 van Amsterdam