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
-
van Halder, Y. (2022, January 18). Efficient sampling and solver enhancement for uncertainty quantification.
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
Jansson, F.R, Edeling, W.N, Attema, J, & Crommelin, D.T. (2021). Assessing uncertainties from physical parameters and modelling choices in an atmospheric large eddy simulation model. Philosophical Transactions of the Royal Society A , 379(2197). doi:10.1098/rsta.2020.0073
Current projects with external funding
-
Valuation Adjustments for Improved Risk Management (ABC-EU-XVA)
-
Physics based ICT: The digital twin in pipelines (DP-Trans)
-
Robust numerical modelling for transient multiphase CO2 transport (SHELL)
-
Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
-
Discretize first, reduce next: a new paradigm to closure for fluid flow simulation (Vidi Sanderse)
Related partners
-
Shell, Amsterdam
-
MeitY
-
Technische Universiteit Eindhoven
-
Universiteit Leiden