Leader of the group Scientific Computing: Benjamin Sanderse.
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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van der Meer, R, Oosterlee, C.W, & Borovykh, A.I. (2022). Optimally weighted loss functions for solving PDEs with Neural Networks. Journal of Computational and Applied Mathematics, 405. doi:10.1016/j.cam.2021.113887
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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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van Halder, Y, Sanderse, B, & Koren, B. (2021). PDE/PDF-informed adaptive sampling for efficient nonintrusive surrogate modeling. International Journal for Uncertainty Quantification, 11(6), 83–108. doi:10.1615/Int.J.UncertaintyQuantification.2021034265
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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Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
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Discretize first, reduce next: a new paradigm to closure for fluid flow simulation (Vidi Sanderse)
Related partners
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MeitY
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Technische Universiteit Eindhoven
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Universiteit Leiden