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
No vacancies currently.
News
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.
New mathematical models for wind turbine load calculations
New mathematical models developed by PhD student Laurent van den Bos can help to determine the best possible way to establish new wind farms. His thesis received the predicate Cum Laude.
Weather forecast techniques help find the perfect oil drill
A new way of processing data from rock measurements could lead to a much more efficient oil extraction. During her PhD research, Sangeetika Ruchi developed a method to infer the most probable rock properties, based on only a few indirect measurements.
Reality check for financial risk assessment tool concludes CWI-led project
One of the most promising tools for assessing financial risks proves to hold up after a thorough mathematical gauging. CWI researcher Ki Wai Chau developed a numerical analysis of complex algorithms that are developed to support financial risk management. His method provides a reality check for those algorithms, paving the way for future applications.
Members
Associated Members
Publications
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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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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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Kumar, P, Sanderse, B, Boorsma, K, & Caboni, M. (2020). Global sensitivity analysis of model uncertainty in aeroelastic wind turbine models. In Journal of Physics: Conference Series. doi:10.1088/1742-6596/1618/4/042034
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Wright, D.W, Richardson, R.A, Edeling, W.N, Lakhlili, J, Sinclair, R, Jancauskas, V, … Coveney, P.V. (2020). Building Confidence in Simulation: Applications of EasyVVUQ. Advanced Theory and Simulations, 3(8). doi:10.1002/adts.201900246
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Rens, E.G, Zeegers, M.T, Rabbers, I, Szabó, A, & Merks, R.M.H. (2020). Autocrine inhibition of cell motility can drive epithelial branching morphogenesis in the absence of growth. Philosophical Transactions of the Royal Society B, 375(1807). doi:10.1098/rstb.2019.0386
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Wing, A, Stauffer, L, Becker, T, Reed, K.A, Ahn, M.-S, Arnold, N.P, … Zhao, M. (2020). Clouds and convective self‐aggregation in a multimodel ensemble of radiative‐convective equilibrium simulations. Journal of Advances in Modeling Earth Systems, 12(9). doi:10.1029/2020MS002138
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van den Oord, G, Jansson, F.R, Pelupessy, F.I, Chertova, M, Grönqvist, J.H, Siebesma, A.P, & Crommelin, D.T. (2020). A Python interface to the Dutch Atmospheric Large-Eddy Simulation. SoftwareX, 12. doi:10.1016/j.softx.2020.100608
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Gugole, F, & Franzke, C.L.E. (2020). Spatial covariance modeling for stochastic subgrid-scale parameterizations using dynamic mode decomposition. Journal of Advances in Modeling Earth Systems, 12. doi:10.1029/2020MS002115
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Richardson, R.A, Wright, D.W, Edeling, W.N, Jancauskas, V, Lakhlili, J, & Coveney, P.V. (2020). EasyVVUQ: A library for verification, validation and uncertainty quantification in high performance computing. Journal of Open Research Software, 8(1), 1–8. doi:10.5334/JORS.303
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