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, and finance. 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
PhD position in advanced numerical methods for fluid flow simulation
Centrum Wiskunde & Informatica (CWI) in Amsterdam has a vacancy in the Scientific Computing research group for a PhD position, on the subject of Discrete approaches to closure modeling for fluid flow simulation.
News
In Memoriam Piet Hemker
With sadness we announce that CWI Fellow and former CWI researcher Piet Hemker passed away on 27 May. Hemker had been working at CWI from 1970–2006 and since 1989 also as a professor at the UvA. He has been a CWI Fellow since 2001 and was named Knight in the Order of the Netherlands Lion in 2006.
Sander Bohté and Kees Oosterlee awarded with NWO Indo-Dutch funding
Sander Bohté (Machine Learning) and Kees Oosterlee (Scientific Computing) have been awarded with funding from NWO’s Indo-Dutch joint research programme for ICT.
CWI develops price models for financial derivatives
The risks of trading complicated financial contracts can be unclear to a certain extent. In order to get a better insight in the determination of prices of such financial derivatives, CWI researcher Anton van der Stoep developed and improved financial mathematical research methods.
WINDTRUE project starts with kick-off meeting at CWI
On 4 March our new WINDTRUE project took to the air, aiming to develop new models that help turbine designers manage large uncertainties in wind and wave behavior, which makes well-dimensioned, cost-optimal designs possible.
Members
Associated Members
Publications
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van der Zwaard, T, Grzelak, L.A, & Oosterlee, C.W. (2021). A computational approach to hedging Credit Valuation Adjustment in a jump-diffusion setting. Applied Mathematics and Computation, 391. doi:10.1016/j.amc.2020.125671
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Salvador Mancho, B, Oosterlee, C.W, & van der Meer, R. (2021). Financial option valuation by unsupervised learning with artificial neural networks. Mathematics, 9(1). doi:10.3390/math9010046
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Sanderse, B. (2020). Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods. Journal of Computational Physics, 421. doi:10.1016/j.jcp.2020.109736
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Rens, E.G, & Merks, R.M.H. (2020). Cell Shape and Durotaxis Explained from Cell-Extracellular Matrix Forces and Focal Adhesion Dynamics. iScience, 23(9). doi:10.1016/j.isci.2020.101488
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Caboni, M, Carrion, M, Rodriguez, C, Schepers, G, Boorsma, K, & Sanderse, B. (2020). Assessment of sensitivity and accuracy of BEM-based aeroelastic models on wind turbine load predictions. In Journal of Physics: Conference Series. doi:10.1088/1742-6596/1618/4/042015
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van den Bos, L.M.M, Sanderse, B, & Bierbooms, W.A.A.M. (2020). Adaptive sampling-based quadrature rules for efficient Bayesian prediction. Journal of Computational Physics, 417. doi:10.1016/j.jcp.2020.109537
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van der Stoep, A.W, Grzelak, L.A, & Oosterlee, C.W. (2020). Collocating volatility: a competative alternative to stochastic local volatility models. International Journal of Theoretical and Applied Finance, 23(6). doi:10.1142/S0219024920500387
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Vos, M.G, Hazeleger, W, Bari, D, Behrens, J, Bendoukha, S., Garcia-Marti, I, … Walton, J. (2020). Open weather and climate science in the digital era. Geoscience Communication, 3, 191–201. doi:10.5194/gc-3-191-2020
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van den Bos, L.M.M, Bierbooms, W.A.A.M, Alexandre, A, Sanderse, B, & van Bussel, G.J.W. (2020). Fatigue design load calculations of the offshore NREL 5 MW benchmark turbine using quadrature rule techniques. Wind Energy, 23(5), 1181–1195. doi:10.1002/we.2470
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Kumar, P, Schmelzer, M. (Martin), & Dwight, R.P. (2020). Stochastic turbulence modeling in RANS simulations via multilevel Monte Carlo. Computers & Fluids, 201. doi:10.1016/j.compfluid.2019.104420
Current projects with external funding
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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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Discretize first, reduce next: a new paradigm to closure for fluid flow simulation (None)
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Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
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Verified Exascale Computing for Multiscale Applications (VECMA)
Related partners
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Max Planck Institute for Informatics
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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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Technische Universiteit Eindhoven
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Brunel University London
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University College London
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Universiteit Leiden
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Universiteit van Amsterdam