Description
Leader of the group Scientific Computing: Daan Crommelin.
The SC groups 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.
Vacancies
PhD position in advanced numerical methods for fluid flow simulation
Centrum Wiskunde & Informatica (CWI) in Amsterdam has a vacancy in the Scientific Computing research group for a PhD position, on the subject of Discrete approaches to closure modeling for fluid flow simulation.
News
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.
CWI researchers involved in two NWO-Groot grants
In the NWO Open Competition ENW-GROOT programme, four CWI researchers received in total two grants to study machine learning and neural networks: Nikhil Bansal, Monique Laurent, Benjamin Sanderse and Leen Stougie.
Members
Associated Members
Publications
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van der Zwaard, T, Grzelak, L.A, & Oosterlee, C.W. (2021). A computational approach to hedging Credit Valuation Adjustment in a jump-diffusion setting. Applied Mathematics and Computation, 391. doi:10.1016/j.amc.2020.125671
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Sanderse, B. (2020). Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods. Journal of Computational Physics, 421. doi:10.1016/j.jcp.2020.109736
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Rens, E.G, & Merks, R.M.H. (2020). Cell Shape and Durotaxis Explained from Cell-Extracellular Matrix Forces and Focal Adhesion Dynamics. iScience, 23(9). doi:10.1016/j.isci.2020.101488
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Caboni, M, Carrion, M, Rodriguez, C, Schepers, G, Boorsma, K, & Sanderse, B. (2020). Assessment of sensitivity and accuracy of BEM-based aeroelastic models on wind turbine load predictions. In Journal of Physics: Conference Series. doi:10.1088/1742-6596/1618/4/042015
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van den Bos, L.M.M, Sanderse, B, & Bierbooms, W.A.A.M. (2020). Adaptive sampling-based quadrature rules for efficient Bayesian prediction. Journal of Computational Physics, 417. doi:10.1016/j.jcp.2020.109537
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van der Stoep, A.W, Grzelak, L.A, & Oosterlee, C.W. (2020). Collocating volatility: a competative alternative to stochastic local volatility models. International Journal of Theoretical and Applied Finance, 23(6). doi:10.1142/S0219024920500387
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Vos, M.G, Hazeleger, W, Bari, D, Behrens, J, Bendoukha, S., Garcia-Marti, I, … Walton, J. (2020). Open weather and climate science in the digital era. Geoscience Communication, 3, 191–201. doi:10.5194/gc-3-191-2020
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van den Bos, L.M.M, Bierbooms, W.A.A.M, Alexandre, A, Sanderse, B, & van Bussel, G.J.W. (2020). Fatigue design load calculations of the offshore NREL 5 MW benchmark turbine using quadrature rule techniques. Wind Energy, 23(5), 1181–1195. doi:10.1002/we.2470
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Kumar, P, Schmelzer, M. (Martin), & Dwight, R.P. (2020). Stochastic turbulence modeling in RANS simulations via multilevel Monte Carlo. Computers & Fluids, 201. doi:10.1016/j.compfluid.2019.104420
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Chau, K.W, Tang, J, & Oosterlee, C.W. (2020). An SGBM-XVA demonstrator: A scalable Python tool for pricing XVA. Journal of Mathematics in Industry, 10(7). doi:10.1186/s13362-020-00073-5
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