Leader of the group Scientific Computing: Benjamin Sanderse.
Vacancies
No vacancies currently.
News
New Shell-CWI project to improve CO2 transport simulations
To help reduce global warming, transport of CO2 for underground storing should be optimized. CWI will develop new mathematical modelling techniques to improve this, in a new research project in collaboration with Shell.
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.
Members
Associated Members
Publications
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Koren, B. (2022). Denker en doener : in memoriam Piet Hemker (1941-2019). Nieuw Archief voor Wiskunde, 23(2), 113–118.
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van Halder, Y. (2022, January 18). Efficient sampling and solver enhancement for uncertainty quantification.
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Boonstra, B.C, & Oosterlee, C.W. (2021). Valuation of electricity storage contracts using the COS method. Applied Mathematics and Computation, 410. doi:10.1016/j.amc.2021.126416
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Gugole, F, Coffeng, L.E, Edeling, W.N, Sanderse, B, de Vlas, S.J, & Crommelin, D.T. (2021). Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model. PLoS Computational Biology, 17(9). doi:10.1371/journal.pcbi.1009355
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Crommelin, D.T, & Edeling, W.N. (2021). Resampling with neural networks for stochastic parameterization in multiscale systems. Physica - D, Nonlinear Phenomena, 422. doi:10.1016/j.physd.2021.132894
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Vassaux, M, Wan, S, Edeling, W.N, & Coveney, P.V. (2021). Ensembles are required to handle aleatoric and parametric uncertainty in molecular dynamics simulation. Journal of Chemical Theory and Computation, 17(8), 5187–5197. doi:10.1021/acs.jctc.1c00526
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van den Oord, G, Chertova, M, Jansson, F.R, Pelupessy, F.I, Siebesma, A.P, & Crommelin, D.T. (2021). Performance optimization and load-balancing modeling for superparametrization by 3D LES. In Proceedings of the Platform for Advanced Scientific Computing Conference (pp. 1–8). doi:10.1145/3468267.3470611
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Suleimenova, D, Arabnejad, H, Edeling, W.N, Coster, D.P, Luk, O.O, Lakhlili, J, … Groen, D. (2021). Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit. Journal of Computational Science, 53. doi:10.1016/j.jocs.2021.101402
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Mücke, N.T, Bohte, S.M, & Oosterlee, C.W. (2021). Reduced order modeling for parameterized time-dependent PDEs using spatially and memory aware deep learning. Journal of Computational Science, 53. doi:10.1016/j.jocs.2021.101408
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den Haan, T.R.B, Chau, K.W, van der Schans, M, & Oosterlee, C.W. (2021). Rule-based strategies for dynamic life cycle investment. European Actuarial Journal. doi:10.1007/s13385-021-00283-0
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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Robust numerical modelling for transient multiphase CO2 transport (SHELL)
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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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Shell, Amsterdam
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MeitY
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Technische Universiteit Eindhoven
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Universiteit Leiden