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 researcher Fredrik Jansson in Science Robotics on robot swarms
Hundreds of small robots can work in a team to create biology-inspired shapes without an underlying master plan, purely based on local communication and movement. This research was published in Science Robotics, with Fredrik Jansson, currently working at CWI, as one of the co-authors.
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.
Current events
PhD defense Anton van der Stoep (Scientific Computing)
- 2019-03-26T12:30:00+01:00
- 2019-03-26T13:30:00+01:00
PhD defense Anton van der Stoep (Scientific Computing)
Start: 2019-03-26 12:30:00+01:00 End: 2019-03-26 13:30:00+01:00
You are cordially invited to the public defense of Anton van der Stoep on his PhD thesis titled:
Pricing and Calibration with Stochastic Local Volatility Models in a Monte Carlo Setting
Promotor: prof. dr. C.W. Oosterlee (CWI, TU Delft)
Co-promotor: dr. ir. L.A. Grzelak (Rabobank, TU Delft)
- Email: Anton van der Stoep
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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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de Leeuw, B.M, Dubinkina, S, Frank, J.E, Steyer, A, Tu, X, & van Vleck, E. (2018). Projected shadowing-based data assimilation. SIAM Journal on Applied Dynamical Systems, 17(4), 2446–2477. doi:10.1137/17M1141163
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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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Sanderse, B, Dubinkina, S, Henkes, R.A.W.M, & Oosterlee, C.W. (2018). Numerical simulation of roll waves in pipelines using the two-fluid model.
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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
Current projects with external funding
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Valuation Adjustments for Improved Risk Management ( ABC-EU-XVA)
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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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Towards cloud-resolving climate simulations
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Excellence in Uncertainty Reduction of Offshore Wind Systems (EUROS)
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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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WIND Turbine Rotor aeroelasticity UncErtainty quantification (WINDTRUE)
Related partners
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DNV GL Netherlands B.V
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FOM
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Max Planck Institute for Informatics
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SE Blades Technology B.V.
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Shell, Amsterdam
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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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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 van Amsterdam