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.
Vacancies
Postdoc on the subject of Uncertainty quantification and calibration of aeroelastic wind turbine models.
Centrum Wiskunde & Informatica (CWI) has a vacancy in the Scientific Computing research group for a Postdoc on the subject of Uncertainty quantification and calibration of aeroelastic wind turbine models.
News
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.
Rare events in sustainable energy grids simulated
The transition to renewable energy sources poses new challenges to the reliability of power grids. As renewable energy is very variable, rare combinations of factors might lead to instabilities and even black-outs. Wander Wadman of Centrum Wiskunde & Informatica (CWI) developed new simulation methods that can determine the probability of such rare events.
Simulations for nuclear fusion
The plasma inside a nuclear fusion reactor is prone to instabilities due to the extreme temperatures and high magnetic fields necessary for nuclear fusion. The future of nuclear fusion as an energy source depends on a large extent on methods to control these instabilities.
European consortium starts research on financial risk models
A European consortium of partners in academia and industry from the Netherlands, Italy and Spain has been granted 1,5 million euro for the Horizon 2020 research project WAKEUPCALL. The project, which is coordinated by the Centrum Wiskunde & Informatica (CWI) in Amsterdam, combines academic expertise in financial mathematics with experience from partners in the finance and insurance industries.
Current events
Probability Seminar Jonas Kremer (Bergische Universität Wuppertal)
- 2018-12-14T11:00:00+01:00
- 2018-12-14T12:00:00+01:00
Probability Seminar Jonas Kremer (Bergische Universität Wuppertal)
Start: 2018-12-14 11:00:00+01:00 End: 2018-12-14 12:00:00+01:00
- Web
- Email: Krzysztof Bisewski
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iCal
Members
Associated Members
Publications
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von Sydow, L, Milovanović, S, Larsson, E, In't Hout, K, Wiktorsson, M, Oosterlee, C.W, … Waldén, J. (2018). BENCHOP–SLV: the BENCHmarking project in Option Pricing–Stochastic and Local Volatility problems. International Journal of Computer Mathematics. doi:10.1080/00207160.2018.1544368
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Ruchi, S, & Dubinkina, S. (2018). Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling. Nonlinear Processes in Geophysics, 25(4), 731–746. doi:10.5194/npg-25-731-2018
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Borregales, M, Kumar, K, Radu, F.A, Rodrigo, C, & Gaspar, F.J. (2018). A partially parallel-in-time fixed-stress splitting method for Biot's consolidation model. Computers & Mathematics with Applications. doi:10.1016/j.camwa.2018.09.005
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Franco, S.R, Rodrigo, C, Gaspar, F.J, & Pinto, M.A.V. (2018). A multigrid waveform relaxation method for solving the poroelasticity equations. Computational and Applied Mathematics, 37(4), 4805–4820. doi:10.1007/s40314-018-0603-9
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Boas, S.E.M, Carvalho, J, van den Broek, M, Weijers, E.M, Goumans, M.-J, Koolwijk, P, & Merks, R.M.H. (2018). A local uPAR-plasmin-TGFβ1 positive feedback loop in a qualitative computational model of angiogenic sprouting explains the in vitro effect of fibrinogen variants. PLoS Computational Biology, 14(7). doi:10.1371/journal.pcbi.1006239
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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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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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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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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, 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
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