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 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.
Vici grants for Nikhil Bansal and Roeland Merks
CWI researchers Nikhil Bansal and Roeland Merks have been awarded Vici grants from NWO. With the 1.5 million euro grant, Bansal and Merks can develop their own research lines in the next five years.
Current events
UvA/CWI Stochastic Seminar: Nikki Sonenberg (Antwerp University)
- 2018-09-27T12:00:00+02:00
- 2018-09-27T13:00:00+02:00
UvA/CWI Stochastic Seminar: Nikki Sonenberg (Antwerp University)
Start: 2018-09-27 12:00:00+02:00 End: 2018-09-27 13:00:00+02:00
Nikki Sonenberg (Antwerp University) - Networks of interacting stochastic fluid models
Stochastic fluid models have been widely used to model the level of a resource that changes over time, where the rate of variation depends on the state of some continuous time Markov process. Latouche and Taylor introduced an approach, using matrix analytic methods and the reduced load approach for loss networks, to analyse networks of fluid models all driven by the same modulating process where the buffers are infinite. We extend the method to networks involving finite buffer models and illustrate the approach by deriving performance measures for a simple network as characteristics such as buffer size are varied. Our results provide insight into the situations where the infinite buffer model is a reasonable approximation to the finite buffer model.
- Web
- Email: Krzysztof Bisewski
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iCal
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, 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, 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.
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