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 joins two large-scale energy projects
Researchers of CWI will participate in the projects ‘Smart Energy Systems in the Built Environment’ and ‘Excellence in Uncertainty Reduction of Offshore Wind Systems (EUROS)’ of the STW Perspective research programmes that were recently awarded.
Stieltjes Prize 2013 for Benjamin Sanderse
Benjamin Sanderse, former PhD Student of CWI, has been awarded the Stieltjes Prize 2013, the prize for the best PhD thesis in mathematics in the Netherlands in 2013. In March 2013 Sanderse defended his thesis 'Energy-conserving discretization methods for the incompressible Navier-Stokes equations' at Eindhoven University of Technology under the supervision of Prof. dr. ir. Barry Koren.
CWI and ExxonMobil Chemical start research on imaging of nanomaterials
Researchers from CWI and ExxonMobil Chemical Europe Inc. in Machelen (Belgium) are starting a joint research program on three-dimensional imaging of nanomaterials.
Five Veni grants for CWI researchers
The Netherlands Organisation for Scientific Research (NWO) has awarded Veni grants to Jop Briët, Jiyin He, Wouter Koolen, Marc Stevens and Xiaodong Zhuge of CWI.
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