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 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.
Debarati Bhaumik developed models that can help make the energy grid ‘renewables-proof’
On 30 May 2018, CWI PhD student Debarati Bhaumik will publicly defend her thesis at the University of Amsterdam. Her work focuses on computational techniques for assessing the reliability of power grids that are integrated with renewable energy sources like wind and solar power.
Tackling messy blood vessels to fight cancer
With a Vici grant of 1.5 million euro, Roeland Merks (CWI and UL) looks for ways to fix messy and leaky blood vessels in tumours. To do so, he combines mathematical simulations and lab experiments in a unique way.
Vici grants for Nikhil Bansal and Roeland Merks
CWI researchers Nikhil Bansal and Roeland Merks have been awarded Vici grants from NWO. With the 1.5 million euro grant, Bansal and Merks can develop their own research lines in the next five years.
Members
Associated Members
Publications
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van den Bos, L.M.M, Sanderse, B, Blonk, L, Bierbooms, W.A.A.M, & Van Bussel, G.J.W. (2018). Efficient ultimate load estimation for offshore wind turbines using interpolating surrogate models. In Modeling and Simulation Technology. doi:10.1088/1742-6596/1037/6/062017
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Bhaumik, D, Crommelin, D.T, & Zwart, A.P. (2018). Mitigation of large power spills by an energy storage device in a stand alone energy system. Journal of Energy Storage, 16, 76–83. doi:10.1016/j.est.2017.12.012
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Baatsen, M.L.J, von der Heydt, A.S, Kliphuis, M, Viebahn, J.P, & Dijkstra, H.A. (2018). Multiple states in the late Eocene ocean circulation. Global and Planetary Change, 163, 18–28. doi:10.1016/j.gloplacha.2018.02.009
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Borovykh, A, Pascucci, A, & Oosterlee, C.W. (2018). Efficient computation of various valuation adjustments under local Lévy models. SIAM Journal on Financial Mathematics, 9(1), 251–273. doi:10.1137/16M1099005
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Luo, P, Rodrigo, C, Gaspar Lorenz, F.J, & Oosterlee, C.W. (2018). Monolithic multigrid method for the coupled Stokes flow and deformable porous medium system. Journal of Computational Physics, 353, 148–168. doi:10.1016/j.jcp.2017.09.062
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Feng, Q, & Oosterlee, C.W. (2018). Computing credit valuation adjustment for Bermudan options with wrong way risk. International Journal of Theoretical and Applied Finance, 20(8). doi:10.1142/S021902491750056X
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Fontanari, A, Cirillo, P, & Oosterlee, C.W. (2018). From Concentration Profiles to Concentration Maps. New tools for the study of loss distributions. Insurance: Mathematics and Economics, 78, 13–29. doi:10.1016/j.insmatheco.2017.11.003
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Ruchi, S, & Dubinkina, S. (2017). Parameter estimation for subsurface flow using ensemble data assimilation.
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Singor, S.N, Boer, A, Alberts, J.S.C, & Oosterlee, C.W. (2017). On the modelling of nested risk-neutral stochastic processes with applications in insurance. Applied Mathematical Finance, 24(4), 302–336. doi:10.1080/1350486X.2017.1378583
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Feng, Q, & Oosterlee, C.W. (2017). Calculation of exposure profiles and sensitivities of options under the Heston and the Heston Hull-White models. In Fields Institute Communications. doi:10.1007/978-1-4939-6969-2_9
Current projects with external funding
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Accurate prediction of slugs in multiphase pipe flow simulation for improved oil and gas production
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Geometric Structure and Data Assimilation
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Probabilistic Uncertainty Assessments in Energy-Related Problems
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Towards cloud-resolving climate simulations
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Uncertainty Quantication in Hydraulic Fracturing using Multi-Level Monte Carlo and Multigrid
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Excellence in Uncertainty Reduction of Offshore Wind Systems (EUROS)
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Efficient numerical methods for deformable porous media. Application to carbon dioxide storage (PORO SOS)
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Rare Event Simulation for Climate Extremes (RESClim)
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Sloshing of Liquefied Natural Gas: subproject Variability (14-10-project2) (SLING)
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Verified Exascale Computing for Multiscale Applications (VECMA)
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Applied mathematics for risk measures in finance and insurance, in the wake of the crisis (WAKEUPCALL)
Related partners
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FOM
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Max Planck Institute for Informatics
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Shell, Amsterdam
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Vortech
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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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Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
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Rijksuniversiteit Groningen
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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 van Amsterdam