Description
The Scientific Computing group at CWI 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.
The SC group organizes the seminar on Machine Learning and Uncertainty Quantification in Scientific Computing.
Vacancies
No vacancies currently.
News
Researchers find substantial uncertainties in Covid-19 pandemic simulations
Computer modelling to forecast Covid-19 mortality contains significant uncertainty in its predictions, according to an international study led by researchers at UCL and CWI. Their article was published in Nature Computational Science on 22 February.
Multiple simulations best for Covid-19 predictions
Computer modelling used to forecast Covid-19 mortality contains significant uncertainty in its predictions, according to a new study led by researchers at UCL and CWI in the Netherlands. This was described in a news item in Nature on 13 November.
Benjamin Sanderse wins Vidi grant to study complex fluid flows
To predict the output of a wind farm, the weather or the blood flow through a heart valve, accurate fluid flow models are needed. CWI researcher Benjamin Sanderse received a Vidi grant from NWO to develop a new generation of such models, based on discrete mathematics.
Cells ‘walk’ to firm ground
A new mathematical model may explain how body cells get their shapes and what makes them move within a tissue. The model provides fundamental knowledge for applications in tissue engineering, amongst other things. The research was executed by Roeland Merks and Lisanne Rens, who were previously affiliated with CWI's Scientific Computing group.
Members
Associated Members
Publications
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Andersson, K.H, & Oosterlee, C.W. (2021). Deep learning for CVA computations of large portfolios of financial derivatives. Applied Mathematics and Computation, 409. doi:10.1016/j.amc.2021.126399
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Salvador Mancho, B, & Oosterlee, C.W. (2021). Corrigendum to “Total value adjustment for a stochastic volatility model. A comparison with the Black–Scholes model”. Applied Mathematics and Computation, 406. doi:10.1016/j.amc.2021.125999
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van den Bos, L.M.M, & Sanderse, B. (2021). A geometrical interpretation of the addition of nodes to an interpolatory quadrature rule while preserving positive weights. Journal of Computational and Applied Mathematics, 391. doi:10.1016/j.cam.2021.113430
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Souto Arias, L.A, & Cirillo, P. (2021). Joint and survivor annuity valuation with a bivariate reinforced urn process. Insurance: Mathematics and Economics, 99, 174–189. doi:10.1016/j.insmatheco.2021.04.004
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Suleimenova, D, Arabnejad, H, Edeling, W.N, & Groen, D. (2021). Sensitivity-driven simulation development: A case study in forced migration. Philosophical Transactions of the Royal Society A , 379(2197). doi:10.1098/rsta.2020.0077
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Kelbij Star, S.K, Sanderse, B, Stabile, G, Rozza, G, & Degroote, J. (2021). Reduced order models for the incompressible Navier-Stokes equations on collocated grids using a ‘discretize-then-project’ approach. International Journal for Numerical Methods in Fluids, 93(8), 2694–2722. doi:10.1002/fld.4994
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Salvador Mancho, B, & Oosterlee, C.W. (2021). Total value adjustment for a stochastic volatility model. A comparison with the Black–Scholes model. Applied Mathematics and Computation, 391. doi:10.1016/j.amc.2020.125489
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Verheul, N, & Crommelin, D.T. (2021). Stochastic parametrization with VARX processes. Communications in Applied Mathematics and Computational Science, 16(1), 33–57. doi:10.2140/CAMCOS.2021.16.33
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Le Floc’h, F.L, & Oosterlee, C.W. (2020). Numerical techniques for the Heston collocated volatility model. Journal of Computational Finance, 24(3), 59–110. doi:10.21314/JCF.2020.401
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Kumar, P, Sanderse, B, Boorsma, K, & Caboni, M. (2020). Global sensitivity analysis of model uncertainty in aeroelastic wind turbine models. In Journal of Physics: Conference Series. doi:10.1088/1742-6596/1618/4/042034
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