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
PhD position in advanced numerical methods for fluid flow simulation
Centrum Wiskunde & Informatica (CWI) in Amsterdam has a vacancy in the Scientific Computing research group for a PhD position, on the subject of Discrete approaches to closure modeling for fluid flow simulation.
News
Extreme events better investigated with new math method
Fortunately, incidents like extreme weather, earthquakes or massive power grid blackouts are a rare occurrence. Analysing properly how likely such rare incidents are to happen can be very valuable, but also very challenging. In his PhD thesis, Krzysztof Bisewski developed mathematical methods that greatly speed up simulations for estimating the probability of rare events.
New data framework illuminates uncertainties in offshore wind farm conditions
Wind speeds and wave heights can have a major effect on offshore wind farms. But because they are correlated, their combined significance for wind farm designs couldn’t be factored in until now. CWI researcher Anne Eggels developed methods which take the effect of such correlations or dependencies into account. Today, she will publicly defend her thesis at the University of Amsterdam.
Scientists develop detailed representation of clouds in weather and climate models
A team of climate researchers and computational experts has developed an innovative method to study cloud dynamics in unprecedented detail in weather and climate models.
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.
Members
Associated Members
Publications
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van der Zwaard, T, Grzelak, L.A, & Oosterlee, C.W. (2021). A computational approach to hedging Credit Valuation Adjustment in a jump-diffusion setting. Applied Mathematics and Computation, 391. doi:10.1016/j.amc.2020.125671
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Salvador Mancho, B, Oosterlee, C.W, & van der Meer, R. (2021). Financial option valuation by unsupervised learning with artificial neural networks. Mathematics, 9(1). doi:10.3390/math9010046
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Sanderse, B. (2020). Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods. Journal of Computational Physics, 421. doi:10.1016/j.jcp.2020.109736
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Rens, E.G, & Merks, R.M.H. (2020). Cell Shape and Durotaxis Explained from Cell-Extracellular Matrix Forces and Focal Adhesion Dynamics. iScience, 23(9). doi:10.1016/j.isci.2020.101488
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Caboni, M, Carrion, M, Rodriguez, C, Schepers, G, Boorsma, K, & Sanderse, B. (2020). Assessment of sensitivity and accuracy of BEM-based aeroelastic models on wind turbine load predictions. In Journal of Physics: Conference Series. doi:10.1088/1742-6596/1618/4/042015
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van den Bos, L.M.M, Sanderse, B, & Bierbooms, W.A.A.M. (2020). Adaptive sampling-based quadrature rules for efficient Bayesian prediction. Journal of Computational Physics, 417. doi:10.1016/j.jcp.2020.109537
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van der Stoep, A.W, Grzelak, L.A, & Oosterlee, C.W. (2020). Collocating volatility: a competative alternative to stochastic local volatility models. International Journal of Theoretical and Applied Finance, 23(6). doi:10.1142/S0219024920500387
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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
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