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 and ExxonMobil Chemical start research on imaging of nanomaterials
Researchers from CWI and ExxonMobil Chemical Europe Inc. in Machelen (Belgium) are starting a joint research program on three-dimensional imaging of nanomaterials.
Five Veni grants for CWI researchers
The Netherlands Organisation for Scientific Research (NWO) has awarded Veni grants to Jop Briët, Jiyin He, Wouter Koolen, Marc Stevens and Xiaodong Zhuge of CWI.
Daan Crommelin professor of Numerical Analysis and Dynamical Systems
Daan Crommelin has been appointed as professor by special appointment of Numerical Analysis and Dynamical Systems at the University of Amsterdam's (UvA) Faculty of Science. The chair was established on behalf of the Stichting voor Hoger Onderwijs in de Toegepaste Wiskunde.
NAW publication on Mathematics and Planet Earth
Daan Crommelin (CWI) and Henk Schuttelaars (TU Delft) were guest editors of a special issue of Nieuw Archief voor Wiskunde (NAW) on Mathematics and Planet Earth that was recently published. Daan Crommelin also wrote a blog post for the Daily Blog of the 'Mathematics of Planet Earth 2013' (MPE) website on this special publication. A summary follows below.
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