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.









Current events

Probability Seminar Cornelia Wichelhaus (TU Darmstadt)

  • 2019-03-22T11:00:00+01:00
  • 2019-03-22T12:00:00+01:00
March 22 Friday

Start: 2019-03-22 11:00:00+01:00 End: 2019-03-22 12:00:00+01:00

F3.20 (KdVI)

Cornelia's research interests are on the intersection of (nonparametric) statistics and stochastic networks.

Nonparametric Estimation in Stochastic Networks  Stochastic networks are interacting systems of nodes with service  stations between which customers move in order to receive service.  The  talk addresses nonparametric estimation problems for these systems in  discrete time. In particular we are interested in estimating the service  time distributions at the nodes based on incomplete observations of the  systems. We assume that we are only able to observe the external  arrivals and departures of customers. We propose two estimation  approaches. The first one is based on the construction of a so-called  sequence of differences and the second utilizes the structure of  cross-covariance functions between specific stochastic processes of the  network.  Both methods lead to deconvolution problems which we solve  explicitly and which lead to consistent estimators. As main properties  we show functional central limit theorems for the estimators in  appropriate discrete spaces.

PhD defence 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 defence 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)

Combined CI/SC Seminar Cristóbal Bertoglio, Bernoulli Institute

  • 2019-04-09T11:00:00+02:00
  • 2019-04-09T12:00:00+02:00
April 9 Tuesday

Start: 2019-04-09 11:00:00+02:00 End: 2019-04-09 12:00:00+02:00


Inverse problems in hemodynamics from MRI

We will present recent advances and future challenges in the field of data-based mathematical modeling of blood flows with data coming from Magnetic Resonance Imaging (MRI). Specifically, we will explore different inverse problems when going from more to less measured data: (a) Pressure maps estimation from 3D+time velocity fields, (b) Parameter estimation from 2D velocity fields (c) extension to parameter estimation from highly undersampled raw MRI data.


Associated Members


Current projects with external funding

  • Advies marktconforme waardering NHG garanties ()
  • 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)
  • 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
  • Stichting Waarborgfonds Eigen Woningen
  • 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