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, finance and biology. 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.
News
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.
WINDTRUE project starts with kick-off meeting at CWI
On 4 March our new WINDTRUE project took to the air, aiming to develop new models that help turbine designers manage large uncertainties in wind and wave behavior, which makes well-dimensioned, cost-optimal designs possible.
CWI researcher Fredrik Jansson in Science Robotics on robot swarms
Hundreds of small robots can work in a team to create biology-inspired shapes without an underlying master plan, purely based on local communication and movement. This research was published in Science Robotics, with Fredrik Jansson, currently working at CWI, as one of the co-authors.
CWI awarded Industrial Doctorates program on Financial Risk Management
Recently, Kees Oosterlee from CWI's Scientific Computing group has been awarded a project from the H2020 European Industrial Doctorates (EID) Program.
Members
Associated Members
Publications
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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
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van den Bos, L.M.M. (2020, February 4). Quadrature methods for wind turbine load calculations.
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Eggels, A.W. (Anne W.), & Crommelin, D.T. (2020). Uncertainty quantification with dependent inputs: Wind and waves. In Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018 (pp. 4099–4110).
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Razaaly, N, Crommelin, D.T, & Congedo, P.M. (2019). Efficient estimation of extreme quantiles using adaptive kriging and importance sampling. International Journal for Numerical Methods in Engineering. doi:10.1002/nme.6300
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Oosterlee, C.W, & Grzelak, L.A. (2019). Mathematical Modeling and Computation in Finance. World Scientific.
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van Halder, Y, Sanderse, B, & Koren, B. (2019). An adaptive minimum spanning tree multielement method for uncertainty quantification of smooth and discontinuous responses. SIAM Journal on Scientific Computing, 41(6), A3624–A3648. doi:10.1137/18M1219643
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Ruchi, S, Dubinkina, S, & Iglesias, M.A. (2019). Transform-based particle filtering for elliptic Bayesian inverse problems. Inverse Problems, 35(11). doi:10.1088/1361-6420/ab30f3
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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Dronesurance
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Sloshing of Liquefied Natural Gas: subproject Variability (14-10-project2) (SLING)
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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)
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WIND Turbine Rotor aeroelasticity UncErtainty quantification (WINDTRUE)
Related partners
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DNV GL Netherlands B.V
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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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Maritiem Research Instituut
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MeitY
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Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
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Rijksuniversiteit Groningen
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Suzlon Blades Technology
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TNO
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Technische Universiteit Eindhoven
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Technische Universiteit Delft
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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