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

Current events

PhD defense Anton van der Stoep (Scientific Computing)

  • 2019-03-26T12:30:00+01:00
  • 2019-03-26T13:30:00+01:00
March 26 Tuesday

Start: 2019-03-26 12:30:00+01:00 End: 2019-03-26 13:30:00+01:00

TU Delft

You are cordially invited to the public defense of Anton van der Stoep on his PhD thesis titled:

Pricing and Calibration with Stochastic Local Volatility Models in a Monte Carlo Setting

Promotor: prof. dr. C.W. Oosterlee (CWI, TU Delft)
Co-promotor: dr. ir. L.A. Grzelak (Rabobank, TU Delft)

Members

Associated Members

Publications

Current projects with external funding

  • Valuation Adjustments for Improved Risk Management ( ABC-EU-XVA)
  • Accurate prediction of slugs in multiphase pipe flow simulation for improved oil and gas production
  • Geometric Structure and Data Assimilation
  • Towards cloud-resolving climate simulations
  • 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
  • SE Blades Technology B.V.
  • Shell, Amsterdam
  • Bull Sas
  • CBK Sci Con Ltd
  • Bayerische Akademie der Wissenschaften
  • Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
  • Rijksuniversiteit Groningen
  • TNO
  • Technische Universiteit Eindhoven
  • Technische Universiteit Delft
  • Brunel University London
  • University College London
  • Universiteit van Amsterdam