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 new calculation methods in collaboration with ING bank and UvA
Centrum Wiskunde & Informatica (CWI) and the University of Amsterdam (UvA) have developed new calculation methods for the estimation of financial risks.
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.
Members
- Krzysztof Bisewski
- Laurent van den Bos
- Jurriaan Buist
- Ki Chau
- Daan Crommelin
- Svetlana Dubinkina
- Wouter Edeling
- Anne Eggels
- Andrea Fontanari
- Francisco Gaspar
- Yous van Halder
- Fredrik Jansson
- Barry Koren
- Prashant Kumar
- Bart de Leeuw
- Roeland Merks
- Kees Oosterlee
- Sangeetika Ruchi
- Benjamin Sanderse
- Jan Viebahn
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