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.
Vacancies
Postdoc on the subject of Uncertainty quantification and calibration of aeroelastic wind turbine models.
Centrum Wiskunde & Informatica (CWI) has a vacancy in the Scientific Computing research group for a Postdoc on the subject of Uncertainty quantification and calibration of aeroelastic wind turbine models.
News
CWI develops new calculation methods in collaboration with ING bank and UvA
Centrum Wiskunde & Informatica (CWI) and the University of Amsterdam (UvA) have developed new calculation methods for the estimation of financial risks.
Dealing with uncertainties in simulations
Understanding uncertainties is crucial when designing computer simulations. Incorporating such uncertainties in simulations and mapping the bandwith of possbile values is the central topic of the inaugural lecture of Daan Crommelin at the University of Amsterdam on Thursday 21 April 2016.
CWI reduces perturbations in tomography
Researcher Folkert Bleichrodt of CWI has developed new methods to improve the applicability of tomography. These methods reduce unwanted perturbations. This allows for applying tomography on a very small scale or based on a small number of measurements.
Current events
Probability Seminar Jonas Kremer (Bergische Universität Wuppertal)
- 2018-12-14T11:00:00+01:00
- 2018-12-14T12:00:00+01:00
Probability Seminar Jonas Kremer (Bergische Universität Wuppertal)
Start: 2018-12-14 11:00:00+01:00 End: 2018-12-14 12:00:00+01:00
- 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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Eggels, A.W, & Crommelin, D.T. (2018). UQ with dependent inputs: wind and waves. In Proceedings of ECCM 6 and ECFD 7.
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Bisewski, K, Crommelin, D.T, & Mandjes, M.R.H. (2018). Controlling the time discretization bias for the supremum of Brownian Motion. ACM Transactions on Modeling and Computer Simulation, 28(3). doi:10.1145/3177775
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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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Rens, E.G. (2018, June 27). Multiscale mathematical biology of cell-extracellular matrix interactions during morphogenesis.
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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
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