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

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…

Current events

Uncertainty Quantification Seminar Ivan Yaroslavtsev (TU Delft)

  • 2019-02-21T15:00:00+01:00
  • 2019-02-21T16:00:00+01:00
February 21 Thursday

Start: 2019-02-21 15:00:00+01:00 End: 2019-02-21 16:00:00+01:00

Korteweg-de Vries Institute, room F1.15

Title: Burkholder-Davis-Gundy inequalities and stochastic integration in UMD Banach spaces

Abstract: In this talk we will present Burkholder--Davis--Gundy inequalities for general UMD Banach space-valued martingales. Namely, we will show that for any UMD Banach space X, for any X-valued martingale M with M_0=0, and for any 1 \leq p < infty:
E sup_{0 \leq s \leq t} ||M_s||^p \eqsim_{p, X} E gamma([M]_t)^p,   t \geq 0,

where [M]_t is the covariation bilinear form of M defined on X* x X* by

[M]_t(x*, y*) = [<M,x*>,<M, y*>]_t,   for x*, y* in X*,

and gamma([M]_t) is the L2-norm of a Gaussian measure on X having [M]_t as its covariance bilinear form.

As a consequence we will extend the theory of vector-valued stochastic integration with respect to a cylindrical Brownian motion by van Neerven, Veraar, and Weis, to the full generality.

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)

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

L120

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.

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