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.
Vacancies
Tenure track position Machine learning and partial differential equations
Centrum Wiskunde & Informatica (CWI) in Amsterdam invites enthusiastic and highly talented researchers to apply for a Tenure track position Machine learning and partial differential equations in the Scientific Computing research group.
News
Smart mobility start-up Skialabs launched by CWI researchers
Traffic flows in cities could be managed much more efficiently thanks to cutting-edge technology pioneered by the new start-up Skialabs. By using huge dataflows collected in cities, the Skialabs platform provides a real-time view on the mobility flows in the city. This allows creating cost-effective and sustainable mobility services that react instantly to the demands of the end-users.
Extreme events better investigated with new math method
Fortunately, incidents like extreme weather, earthquakes or massive power grid blackouts are a rare occurrence. Analysing properly how likely such rare incidents are to happen can be very valuable, but also very challenging. In his PhD thesis, Krzysztof Bisewski developed mathematical methods that greatly speed up simulations for estimating the probability of rare events.
New data framework illuminates uncertainties in offshore wind farm conditions
Wind speeds and wave heights can have a major effect on offshore wind farms. But because they are correlated, their combined significance for wind farm designs couldn’t be factored in until now. CWI researcher Anne Eggels developed methods which take the effect of such correlations or dependencies into account. Today, she will publicly defend her thesis at the University of Amsterdam.
Scientists develop detailed representation of clouds in weather and climate models
A team of climate researchers and computational experts has developed an innovative method to study cloud dynamics in unprecedented detail in weather and climate models.
Current events
PhD Defence Sangeetika Ruchi (SC)
- 2020-01-20T12:45:00+01:00
- 2020-01-20T13:30:00+01:00
PhD Defence Sangeetika Ruchi (SC)
Start: 2020-01-20 12:45:00+01:00 End: 2020-01-20 13:30:00+01:00
You are cordially invited to the public defence of Sangeetika Ruchi on her thesis:
"Parameter Estimation in Random Energy Systems using Data Assimilation"
Promotor: prof.dr.ir. Jason Frank (UU)
co-promotor: dr. Svetlana Dubinkina (CWI)
- Email: Sangeetika Ruchi
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iCal
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 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
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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.
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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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