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
SC Seminar Deepesh Toshniwal (Univ. of Texas ICES)
- 2018-10-18T11:00:00+02:00
- 2018-10-18T12:00:00+02:00
SC Seminar Deepesh Toshniwal (Univ. of Texas ICES)
Start: 2018-10-18 11:00:00+02:00 End: 2018-10-18 12:00:00+02:00
Date: Thursday 18 October
Time: 11h00
Room: L120
Speaker: Deepesh Toshniwal (ICES, The University of Texas at Austin, working with prof. T. Hughes)
Title: High-order structure-preserving splines for incompressible flow simulation
Abstract: In this talk, we will discuss the construction of high-order spline basis functions that span gradient, curl and divergence conforming spaces with applications to electromagnetism and fluid flows in mind. Stability of the resulting numerical schemes is complemented by pointwise satisfaction of conservation laws — such as mass conservation in incompressible flow simulations — on arbitrarily curved geometries even at the coarsest levels of discretization. Examples demonstrating the robustness and applicability of the spline spaces will be presented. In particular, they will be used to simulate Stokes flow on curved surfaces that contain parameterization singularities.
- Web
- Email: Benjamin Sanderse
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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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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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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
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