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 defence Anton van der Stoep (Scientific Computing)
- 2019-03-26T12:30:00+01:00
- 2019-03-26T13:30:00+01:00
PhD defence 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 defence 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
CI/SC Seminar Cristóbal Bertoglio, Bernoulli Institute
- 2019-04-09T11:00:00+02:00
- 2019-04-09T12:00:00+02:00
CI/SC Seminar Cristóbal Bertoglio, Bernoulli Institute
Start: 2019-04-09 11:00:00+02:00 End: 2019-04-09 12:00:00+02:00
Inverse problems in hemodynamics from MRI
We will present recent advances and future challenges in the field of data-based mathematical modeling of blood flows with data coming from Magnetic Resonance Imaging (MRI). Specifically, we will explore different inverse problems when going from more to less measured data: (a) Pressure maps estimation from 3D+time velocity fields, (b) Parameter estimation from 2D velocity fields (c) extension to parameter estimation from highly undersampled raw MRI data.
Members
- Krzysztof Bisewski
- Anastasia Borovykh
- Laurent van den Bos
- Jurriaan Buist
- Daan Crommelin
- Svetlana Dubinkina
- Wouter Edeling
- Anne Eggels
- Yous van Halder
- Fredrik Jansson
- Barry Koren
- Prashant Kumar
- Bart de Leeuw
- Hemaditya Malla
- Roeland Merks
- Kees Oosterlee
- Sangeetika Ruchi
- Beatriz Salvador Mancho
- Benjamin Sanderse
Associated Members
Publications
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Eggels, A.W, & Crommelin, D.T. (2019). Quantifying data dependencies with Rényi mutual information and minimum spanning trees. Entropy, 21(2). doi:10.3390/e21020100
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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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Borovykh, A, Bohte, S.M, & Oosterlee, C.W. (2018). Dilated convolutional neural networks for time series forecasting. Journal of Computational Finance. doi:10.21314/JCF.2019.358
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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, Misra, S, Dubinkina, S, Henkes, R.A.W.M, & Oosterlee, C.W. (2018). Numerical simulation of roll waves in pipelines using the two-fluid model. In 11th North American Conference on Multiphase Technology (pp. 373–386).
Current projects with external funding
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Advies marktconforme waardering NHG garanties ()
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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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Valuation Adjustments for Improved Risk Management (ABC-EU-XVA)
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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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Stichting Waarborgfonds Eigen Woningen
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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