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.
News
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.
CWI develops new calculation methods in collaboration with ING bank and UvA
Centrum Wiskunde & Informatica (CWI) and the University of Amsterdam (UvA) have developed new calculation methods for the estimation of financial risks.
Dealing with uncertainties in simulations
Understanding uncertainties is crucial when designing computer simulations. Incorporating such uncertainties in simulations and mapping the bandwith of possbile values is the central topic of the inaugural lecture of Daan Crommelin at the University of Amsterdam on Thursday 21 April 2016.
Current events
The forty-fourth Woudschoten Conference
- 2019-10-09T08:30:00+02:00
- 2019-10-11T12:00:00+02:00
The forty-fourth Woudschoten Conference
Start: 2019-10-09 08:30:00+02:00 End: 2019-10-11 12:00:00+02:00
Dear SCS member,
We are pleased to announce the registration for the 44th Woudschoten Conference is now open.
Note: You will be asked to pay directly at the registration, with iDEAL, Mister Cash or credit card. Please have your payment ready. iDEAL or Mister Cash are preferred.
More information about the prices and packages can be found here.
We are very grateful to our sponsors NWO, VORtech, 4TU.AMI, NDNS+ and Sioux Lime.
For further information please contact the secretariat of the organizing committee:
Martine Anholt
CWI - Centrum Wiskunde & Informatica
Organizing committee Woudschoten Conference
Science Park 123, 1098 XG Amsterdam
P.O. Box 94079, NL-1090 GB Amsterdam
Telephone Martine Anholt: +31 20 592 4058
- Web
- Email: Martine Anholt
- Phone: +31 20 5924058
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iCal
PhD defense Anne Eggels (SC)
- 2019-11-06T10:00:00+01:00
- 2019-11-06T11:00:00+01:00
PhD defense Anne Eggels (SC)
Start: 2019-11-06 10:00:00+01:00 End: 2019-11-06 11:00:00+01:00
You are cordially invited to the public defense of Anne Eggels on her thesis:
Uncertainty quantification with dependent input data
including applications to offshore wind farm
Promotor: Prof.dr. Daan Crommelin
Co-promotor: prof.dr.ir. Barry Koren
Members
Associated Members
Publications
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Kumar, P. (2019, July 16). Multilevel solvers for stochastic fluid flows.
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Groen, D, Richardson, R.A, Wright, D.W, Jancauskas, V, Sinclair, R, Karlshoefer, P, … Coveney, P.V. (2019). Introducing VECMAtk - Verification, validation and uncertainty quantification for multiscale and HPC simulations. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 479–492). doi:10.1007/978-3-030-22747-0_36
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Sanderse, B, & Veldman, A.E.P. (2019). Constraint-consistent Runge–Kutta methods for one-dimensional incompressible multiphase flow.
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Viebahn, J.P, Crommelin, D.T, & Dijkstra, H.A. (2019). Towards a turbulence closure based on energy modes. Journal of Physical Oceanography, 49(4), 1075–1097. doi:10.1175/JPO-D-18-0117.1
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Wolff, H.B, Davidson, L.A, & Merks, R.M.H. (2019). Adapting a plant tissue model to animal development: Introducing cell sliding into VirtualLeaf. Bulletin of Mathematical Biology, 1–20. doi:10.1007/s11538-019-00599-9
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van der Stoep, A.W. (2019, March 26). Pricing and calibration with stochastic local volatility models in a Monte Carlo setting.
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Bisewski, K.L, Crommelin, D.T, & Mandjes, M.R.H. (2018). Simulation-based assessment of the stationary tail distribution of a stochastic differential equation. In Proceedings of the 2018 Winter Simulation Conference.
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Suárez-Taboada, M, Witteveen, J.A.S, Grzelak, L.A, & Oosterlee, C.W. (2018). Uncertainty quantification and Heston model. Journal of Mathematics in Industry, 8(1). doi:10.1186/s13362-018-0047-2
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Jüling, A, Viebahn, J.P, Drijfhout, S.S, & Dijkstra, H.A. (2018). Energetics of the Southern Ocean Mode. Journal of Geophysical Research: Oceans. doi:10.1029/2018JC014191
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Rodrigo, C, Hu, X, Ohm, P, Adler, J.H, Gaspar, F.J, & Zikatanov, L.T. (2018). New stabilized discretizations for poroelasticity and the Stokes’ equations. Computer Methods in Applied Mechanics and Engineering, 341, 467–484. doi:10.1016/j.cma.2018.07.003
Current projects with external funding
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Accurate prediction of slugs in multiphase pipe flow simulation for improved oil and gas production
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Towards cloud-resolving climate simulations
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Valuation Adjustments for Improved Risk Management (ABC-EU-XVA)
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Physics based ICT: The digital twin in pipelines (DP-Trans)
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Excellence in Uncertainty Reduction of Offshore Wind Systems / Loads and Damage (EUROS)
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Rare Event Simulation for Climate Extremes (RESClim)
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Sloshing of Liquefied Natural Gas: subproject Variability (14-10-project2) (SLING)
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Verified Exascale Computing for Multiscale Applications (VECMA)
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WIND Turbine Rotor aeroelasticity UncErtainty quantification (WINDTRUE)
Related partners
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DNV GL Netherlands B.V
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FOM
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Max Planck Institute for Informatics
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Shell, Amsterdam
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Bull Sas
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CBK Sci Con Ltd
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Bayerische Akademie der Wissenschaften
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MeitY
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Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
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Rijksuniversiteit Groningen
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Suzlon Blades Technology
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TNO
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Technische Universiteit Eindhoven
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Technische Universiteit Delft
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Brunel University London
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University College London
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Universiteit van Amsterdam