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 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.
More accurate climate predictions
Predicting the future climate is a grand mathematical challenge. Our climate is a chaotic dynamical system in which minor disturbances can have a major impact in the longterm. Researcher Keith Myerscough of CWI has developed new simulation methods to improve the accuracy of such long term predictions.
Rare events in sustainable energy grids simulated
The transition to renewable energy sources poses new challenges to the reliability of power grids. As renewable energy is very variable, rare combinations of factors might lead to instabilities and even black-outs. Wander Wadman of Centrum Wiskunde & Informatica (CWI) developed new simulation methods that can determine the probability of such rare events.
Simulations for nuclear fusion
The plasma inside a nuclear fusion reactor is prone to instabilities due to the extreme temperatures and high magnetic fields necessary for nuclear fusion. The future of nuclear fusion as an energy source depends on a large extent on methods to control these instabilities.
Current events
Uncertainty Quantification Seminar Ivan Yaroslavtsev (TU Delft)
- 2019-02-21T15:00:00+01:00
- 2019-02-21T16:00:00+01:00
Uncertainty Quantification Seminar Ivan Yaroslavtsev (TU Delft)
Start: 2019-02-21 15:00:00+01:00 End: 2019-02-21 16:00:00+01:00
Title: Burkholder-Davis-Gundy inequalities and stochastic integration in UMD Banach spaces
Abstract: In this talk we will present Burkholder--Davis--Gundy inequalities for general UMD Banach space-valued martingales. Namely, we will show that for any UMD Banach space X, for any X-valued martingale M with M_0=0, and for any 1 \leq p < infty:
E sup_{0 \leq s \leq t} ||M_s||^p \eqsim_{p, X} E gamma([M]_t)^p, t \geq 0,
where [M]_t is the covariation bilinear form of M defined on X* x X* by
[M]_t(x*, y*) = [<M,x*>,<M, y*>]_t, for x*, y* in X*,
and gamma([M]_t) is the L2-norm of a Gaussian measure on X having [M]_t as its covariance bilinear form.
As a consequence we will extend the theory of vector-valued stochastic integration with respect to a cylindrical Brownian motion by van Neerven, Veraar, and Weis, to the full generality.
- Web
- Email: Krzysztof Bisewski
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iCal
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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Bisewski, K, Crommelin, D.T, & Mandjes, M.R.H. (2018). Simulation-based assessment of the stationary tail distribution of a stochastic differential equation. In Proceedings of the 2018 Winter Simulation Conference.
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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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Jüling, A, Viebahn, J.P, Drijfhout, S.S, & Dijkstra, H.A. (2018). Energetics of the Southern Ocean Mode. Journal of Geophysical Research: Oceans. doi:10.1029/2018JC014191
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Viebahn, J.P, Crommelin, D.T, & Dijkstra, H.A. (2018). Towards a turbulence closure based on energy modes. Journal of Physical Oceanography. doi:10.1175/JPO-D-18-0117.1
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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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Jansson, F, van den Oord, G.J.W.M, Siebesma, A.P, & Crommelin, D.T. (2018). Resolving clouds in a global atmosphere model - a multiscale approach with nested models. doi:10.1109/eScience.2018.00043
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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
Current projects with external funding
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Valuation Adjustments for Improved Risk Management ( ABC-EU-XVA)
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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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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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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