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
Dealing with uncertainties in simulations
Understanding uncertainties is crucial when designing computer simulations. Incorporating such uncertainties in simulations and mapping the bandwith of possbile values is the central topic of the inaugural lecture of Daan Crommelin at the University of Amsterdam on Thursday 21 April 2016.
CWI reduces perturbations in tomography
Researcher Folkert Bleichrodt of CWI has developed new methods to improve the applicability of tomography. These methods reduce unwanted perturbations. This allows for applying tomography on a very small scale or based on a small number of measurements.
More accurate climate predictions
Predicting the future climate is a grand mathematical challenge. Our climate is a chaotic dynamical system in which minor disturbances can have a major impact in the longterm. Researcher Keith Myerscough of CWI has developed new simulation methods to improve the accuracy of such long term predictions.
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