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
Two CWI scientists receive JCF Young Researcher Award
CWI scientists Dr. Anastasia Borovykh and Dr. Beatriz Salvador have received the JCF Young Researcher Award, granted by the Journal of Computational Finance. Both researchers were awarded for their outstanding work, which they presented during the International Conference on Computational Finance.
In Memoriam Piet Hemker
With sadness we announce that CWI Fellow and former CWI researcher Piet Hemker passed away on 27 May. Hemker had been working at CWI from 1970–2006 and since 1989 also as a professor at the UvA. He has been a CWI Fellow since 2001 and was named Knight in the Order of the Netherlands Lion in 2006.
Sander Bohté and Kees Oosterlee awarded with NWO Indo-Dutch funding
Sander Bohté (Machine Learning) and Kees Oosterlee (Scientific Computing) have been awarded with funding from NWO’s Indo-Dutch joint research programme for ICT.
CWI develops price models for financial derivatives
The risks of trading complicated financial contracts can be unclear to a certain extent. In order to get a better insight in the determination of prices of such financial derivatives, CWI researcher Anton van der Stoep developed and improved financial mathematical research methods.
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