Description
Leader of the group Scientific Computing: Benjamin Sanderse.
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, reduced order modeling, data assimilation, and stochastic multiscale modeling. 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
Members
Associated Members
Publications
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Groen, D, Arabnejad, H, Suleimenova, D, Edeling, W.N, Raffin, E, Xue, Y, … Coveney, P.V. (2023). FabSim3: An automation toolkit for verified simulations using high performance computing. Computer Physics Communications, 283, 108596.1–108596.18. doi:10.1016/j.cpc.2022.108596
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Versluis, D.M, Schoemaker, R, Looijesteijn, E, Muysken, D, Jeurink, P.V, Paques, M, … Merks, R.M.H. (2022). A multiscale spatiotemporal model including a switch from aerobic to anaerobic metabolism reproduces succession in the early infant gut microbiota. mSystems, 7(5), e00446‐22.1–e00446‐22.24. doi:10.1128/msystems.00446-22
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Liu, S, Leitao Rodriguez, Á, Borovykh, A.I, & Oosterlee, C.W. (2022). On a neural network to extract implied information from American options. Applied Mathematical Finance, 28(5), 449–475. doi:10.1080/1350486X.2022.2097099
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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
Current projects with external funding
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Physics based ICT: The digital twin in pipelines (DP-Trans)
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Learning small closure models for large multiscale problems. (None)
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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