Description

Leader of the group Scientific Computing: Daan Crommelin.

The world is full of uncertainties. Being able to assess those uncertainties and their impact on predictions is critical for many real-world problems. Our research group works to investigate and develop methods that contribute to a better understanding of hard-to-predict developments in vital areas such as climate, energy, and finance. It’s all about finding efficient mathematical solutions for complex problems and thereby increasing understanding. With the computational methods we develop, it is possible to quantify the range of potential outcomes of systems that are extremely difficult to predict, enabling better forecasting. Our work is targeted in particular at applications in energy systems, finance and climate science. We always aim to make the connection between fundamental research and practical application.

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News

Instable blood supply may help healthy cells compete with tumor cells

Instable blood supply may help healthy cells compete with tumor cells

Researchers of CWI’s Scientific Computing group have found that instabilities in the blood supply in cancerous tissue can, surprisingly, lead to a less favorable environment for tumor cells. Their findings shed light on the potential negative side effects of current treatments that aim to actually normalize the blood supply in cancerous tissues.

Instable blood supply may help healthy cells compete with tumor cells - Read More…

CWI researcher simulates complex financial developments, from interest rates to the possibility of bankruptcy

CWI researcher simulates complex financial developments, from interest rates to the possibility of bankruptcy

PhD student Alvaro Leitao Rodriguez proposes new methods to tackle complex problems in the financial sector. With these methods, Leitao Rodriguez successfully simulates the movements of the interest rates in the Foreign eXchange (FX) markets and evaluates corresponding risks.

CWI researcher simulates complex financial developments, from interest rates to the possibility of bankruptcy - Read More…

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…

Current events

SC Seminar Michael Kirk, NASA Goddard Space Flight Center

  • 2017-12-05T15:00:00+01:00
  • 2017-12-05T16:00:00+01:00
December 5 Tuesday

Start: 2017-12-05 15:00:00+01:00 End: 2017-12-05 16:00:00+01:00

L120, CWI

Michael S. F. Kirk (NASA Goddard Space Flight Center / Catholic University of America)

Poisson-Gaussian Noise Modeling in Solar Images

All digital images are corrupted by noise. In most solar imaging, we have the luxury of high photon counts and low background contamination, which when combined with carful calibration, minimize much of the impact noise has on the measurement. Outside high-intensity regions, such as in coronal holes, the noise component can become significant and complicate feature recognition and segmentation. We create a practical estimate of noise in the Solar Dynamics Observatory’s Atmospheric Imaging Assembly (SDO AIA) images across the detector CCD. A Poisson-Gaussian model of noise is well suited in the digital imaging environment due to the statistical distributions of photons and the characteristics of the CCD. Using the dark and flat field calibration images, the level-1 AIA images, and readout noise measurements, we construct a maximum-a-posteriori estimation of the expected error in the AIA images. These estimations of noise not only provide a clearer view of solar features in AIA, but they are also relevant to error characterizations of other solar images.

Members

Associated Members

Publications

Current projects with external funding

  • Accurate prediction of slugs in multiphase pipe flow simulation for improved oil and gas production
  • Geometric Structure and Data Assimilation
  • Probabilistic Uncertainty Assessments in Energy-Related Problems
  • Realibilty and Robustness of Power Grids with Uncertain Generation
  • Stochastic models for unresolved scales in geophysical flows
  • Towards cloud-resolving climate simulations
  • Uncertainty Quantication in Hydraulic Fracturing using Multi-Level Monte Carlo and Multigrid
  • Excellence in Uncertainty Reduction of Offshore Wind Systems (EUROS)
  • Efficient numerical methods for deformable porous media. Application to carbon dioxide storage (PORO SOS)
  • Rare Event Simulation for Climate Extremes (RESClim)
  • Sloshing of Liquefied Natural Gas: subproject Variability (14-10-project2) (SLING)
  • Applied mathematics for risk measures in finance and insurance, in the wake of the crisis (WAKEUPCALL)

Related partners

  • FOM
  • Shell, Amsterdam
  • Vortech
  • Rijksuniversiteit Groningen
  • Technische Universiteit Eindhoven
  • Technische Universiteit Delft