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
More accurate climate predictions
Predicting the future climate is a grand mathematical challenge. Our climate is a chaotic dynamical system in which minor disturbances can have a major impact in the longterm. Researcher Keith Myerscough of CWI has developed new simulation methods to improve the accuracy of such long term predictions.
Rare events in sustainable energy grids simulated
The transition to renewable energy sources poses new challenges to the reliability of power grids. As renewable energy is very variable, rare combinations of factors might lead to instabilities and even black-outs. Wander Wadman of Centrum Wiskunde & Informatica (CWI) developed new simulation methods that can determine the probability of such rare events.
Simulations for nuclear fusion
The plasma inside a nuclear fusion reactor is prone to instabilities due to the extreme temperatures and high magnetic fields necessary for nuclear fusion. The future of nuclear fusion as an energy source depends on a large extent on methods to control these instabilities.
European consortium starts research on financial risk models
A European consortium of partners in academia and industry from the Netherlands, Italy and Spain has been granted 1,5 million euro for the Horizon 2020 research project WAKEUPCALL. The project, which is coordinated by the Centrum Wiskunde & Informatica (CWI) in Amsterdam, combines academic expertise in financial mathematics with experience from partners in the finance and insurance industries.
Current events
PhD Defence Laurent van den Bos (SC)
- 2020-02-04T12:00:00+01:00
- 2020-02-04T14:00:00+01:00
PhD Defence Laurent van den Bos (SC)
Start: 2020-02-04 12:00:00+01:00 End: 2020-02-04 14:00:00+01:00
You are cordially invited to the public defence of Laurent van den Bos on his thesis:
Quadrature Methods for Wind Turbine Load Calculations
Promotor: Prof. dr. G. J.W. van Bussel (TUD)
Co-promotoren: dr.ir. W. A. A. M. Bierbooms (TUD) en dr.ir. B. Sanderse (CWI)
Time schedule
12:00 - 12:20 Introductory talk
12:30 - 13:30 Defense
13:45 - 14:00 Graduation ceremony
14:00 - Reception (in the same building)
- Email: Laurent van den Bos
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iCal
Members
Associated Members
Publications
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van den Bos, L.M.M. (2020, February 4). Quadrature methods for wind turbine load calculations.
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van Halder, Y, Sanderse, B, & Koren, B. (2019). An adaptive minimum spanning tree multielement method for uncertainty quantification of smooth and discontinuous responses. SIAM Journal on Scientific Computing, 41(6), A3624–A3648. doi:10.1137/18M1219643
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Ruchi, S, Dubinkina, S, & Iglesias, M.A. (2019). Transform-based particle filtering for elliptic Bayesian inverse problems. Inverse Problems, 35. doi:10.1088/1361-6420/ab30f3
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Liu, S, Borovykh, A.I, Grzelak, L.A, & Oosterlee, C.W. (2019). A neural network-based framework for financial model calibration. Journal of Mathematics in Industry, 9(1). doi:10.1186/s13362-019-0066-7
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Borovykh, A.I, Oosterlee, C.W, & Bohte, S.M. (2019). Generalization in fully-connected neural networks for time series forecasting. Journal of Computational Science, 36, 1–15. doi:10.1016/j.jocs.2019.07.007
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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
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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Dronesurance
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Excellence in Uncertainty Reduction of Offshore Wind Systems / Loads and Damage (EUROS)
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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