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
Kees Oosterlee new editor-in-chief of Journal of Computational Finance
Kees Oosterlee is the new Editor-in-Chief of The Journal of Computational Finance. This international refereed journal focuses on the advances in numerical and computational techniques in pricing, hedging and risk management of financial instruments. Oosterlee will be taking responsibility of selecting papers for the journal from the Fall 2013 issue and onwards.
Scientific Computing group nominated for Huibregtsen Prize
CWI’s research group Scientific Computing, led by mathematician Kees Oosterlee, is nominated for the Huibregtsen Prize 2013. The Huibregtsen Prize is granted for the best scientific research in the Netherlands that is remarkably innovative and has a valuable social impact.
CWI research nominated for Huibregtsen Prize
Research from CWI’s research group Scientific Computing, led by mathematician Kees Oosterlee, is nominated for the Huibregtsen Prize 2013.
Solar cells imaged at the nanoscale in 3D
Researchers of Centrum Wiskunde & Informatica (CWI) in Amsterdam succeeded in imaging the three-dimensional structure of solar cells with unprecedented accuracy. The results of this joint research with colleagues from the University of California in Davis and the Pacific Northwest National Laboratory are published today in the journal Advanced Functional Materials.
Members
Associated Members
Publications
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Suárez-Taboada, M, Witteveen, J.A.S, Grzelak, L.A, & Oosterlee, C.W. (2018). Uncertainty quantification and Heston model. Journal of Mathematics in Industry, 8(1). doi:10.1186/s13362-018-0047-2
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Rodrigo, C, Hu, X, Ohm, P, Adler, J.H, Gaspar Lorenz, F.J, & Zikatanov, L.T. (2018). New stabilized discretizations for poroelasticity and the Stokes’ equations. Computer Methods in Applied Mechanics and Engineering, 341, 467–484. doi:10.1016/j.cma.2018.07.003
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Kumar, P, Luo, P, Gaspar Lorenz, F.J, & Oosterlee, C.W. (2018). A multigrid multilevel Monte Carlo method for transport in the Darcy–Stokes system. Journal of Computational Physics, 371, 382–408. doi:10.1016/j.jcp.2018.05.046
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Fontanari, A, Taleb, N.N, & Cirillo, P. (2018). Gini estimation under infinite variance. Physica A: Statistical Mechanics and its Applications, 502, 256–269. doi:10.1016/j.physa.2018.02.102
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Grzelak, L.A, Witteveen, J.A.S, Suárez-Taboada, M, & Oosterlee, C.W. (2018). The stochastic collocation Monte Carlo sampler: highly efficient sampling from ‘expensive’ distributions. Quantitative Finance. doi:10.1080/14697688.2018.1459807
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Bhaumik, D. (2018, May 30). Computational techniques for assessing power grids with wind energy and storage.
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Bhaumik, D, Crommelin, D.T, Kapodistria, S, & Zwart, A.P. (2018). Hidden Markov Models for wind farm power output. IEEE Transactions on Sustainable Energy. doi:10.1109/TSTE.2018.2834475
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Colldeforns-Papiol, G, Ortiz Gracia, L, & Oosterlee, C.W. (2018). Quantifying credit portfolio losses under multi-factor models. International Journal of Computer Mathematics. doi:10.1080/00207160.2018.1447666
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Maree, S.C, Ortiz Gracia, L, & Oosterlee, C.W. (2018). Fourier and wavelet option pricing methods. In M.A.H Dempster, J Kanniainen, J Keane, & E Vynckier (Eds.), High-Performance Computing in Finance: Problems, Methods, and Solutions (pp. 249–272). doi:10.1201/9781315372006
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Eggels, A.W, & Crommelin, D.T. (2018). Quantifying dependencies for sensitivity analysis with multivariate input sample data.