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
Scientists develop detailed representation of clouds in weather and climate models
A team of climate researchers and computational experts has developed an innovative method to study cloud dynamics in unprecedented detail in weather and climate models.
Two CWI scientists receive JCF Young Researcher Award
CWI scientists Dr. Anastasia Borovykh and Dr. Beatriz Salvador have received the JCF Young Researcher Award, granted by the Journal of Computational Finance. Both researchers were awarded for their outstanding work, which they presented during the International Conference on Computational Finance.
In Memoriam Piet Hemker
With sadness we announce that CWI Fellow and former CWI researcher Piet Hemker passed away on 27 May. Hemker had been working at CWI from 1970–2006 and since 1989 also as a professor at the UvA. He has been a CWI Fellow since 2001 and was named Knight in the Order of the Netherlands Lion in 2006.
Sander Bohté and Kees Oosterlee awarded with NWO Indo-Dutch funding
Sander Bohté (Machine Learning) and Kees Oosterlee (Scientific Computing) have been awarded with funding from NWO’s Indo-Dutch joint research programme for ICT.
Current events
PhD defense Anne Eggels (SC)
- 2019-11-06T10:00:00+01:00
- 2019-11-06T11:30:00+01:00
PhD defense Anne Eggels (SC)
Start: 2019-11-06 10:00:00+01:00 End: 2019-11-06 11:30:00+01:00
You are cordially invited to the public defense of Anne Eggels on her thesis:
Uncertainty quantification with dependent input data
including applications to offshore wind farm
Promotor: Prof.dr. Daan Crommelin
Co-promotor: prof.dr.ir. Barry Koren
PhD defense Ki Wai Chau (SC)
- 2020-01-16T10:00:00+01:00
- 2020-01-16T11:00:00+01:00
PhD defense Ki Wai Chau (SC)
Start: 2020-01-16 10:00:00+01:00 End: 2020-01-16 11:00:00+01:00
You are cordially invited to the public defense of Ki Wai Chau on his PhD thesis titled:
Numerical Finance with Backward Stochastic Differential Equations
Promotor: prof. dr. Cornelis W. Oosterlee (CWI, TU Delft)
- Email: Ki Wai Chua
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iCal
Members
Associated Members
Publications
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Liu, S, Borovykh, A.I, Grzelak, L.A, & Oosterlee, C.W. (2019). A neural network-based framework for financial model calibration. Journal of Mathematics in Industry, 9(1). doi:10.1186/s13362-019-0066-7
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Borovykh, A.I, Oosterlee, C.W, & Bohte, S.M. (2019). Generalization in fully-connected neural networks for time series forecasting. Journal of Computational Science, 36, 1–15. doi:10.1016/j.jocs.2019.07.007
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Kumar, P. (2019, July 16). Multilevel solvers for stochastic fluid flows.
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Groen, D, Richardson, R.A, Wright, D.W, Jancauskas, V, Sinclair, R, Karlshoefer, P, … Coveney, P.V. (2019). Introducing VECMAtk - Verification, validation and uncertainty quantification for multiscale and HPC simulations. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 479–492). doi:10.1007/978-3-030-22747-0_36
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Sanderse, B, & Veldman, A.E.P. (2019). Constraint-consistent Runge–Kutta methods for one-dimensional incompressible multiphase flow.
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Viebahn, J.P, Crommelin, D.T, & Dijkstra, H.A. (2019). Towards a turbulence closure based on energy modes. Journal of Physical Oceanography, 49(4), 1075–1097. doi:10.1175/JPO-D-18-0117.1
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Wolff, H.B, Davidson, L.A, & Merks, R.M.H. (2019). Adapting a plant tissue model to animal development: Introducing cell sliding into VirtualLeaf. Bulletin of Mathematical Biology, 1–20. doi:10.1007/s11538-019-00599-9
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van der Stoep, A.W. (2019, March 26). Pricing and calibration with stochastic local volatility models in a Monte Carlo setting.
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Bisewski, K.L, 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
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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Towards cloud-resolving climate simulations
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Valuation Adjustments for Improved Risk Management (ABC-EU-XVA)
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Physics based ICT: The digital twin in pipelines (DP-Trans)
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Excellence in Uncertainty Reduction of Offshore Wind Systems / Loads and Damage (EUROS)
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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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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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MeitY
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Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
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Rijksuniversiteit Groningen
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Suzlon Blades Technology
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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