Leader of the group Scientific Computing: Benjamin Sanderse.
Vacancies
No vacancies currently.
News
CWI researcher simulates complex financial developments, from interest rates to the possibility of bankruptcy
PhD student Alvaro Leitao Rodriguez proposes new methods to tackle complex problems in the financial sector. With these methods, Leitao Rodriguez successfully simulates the movements of the interest rates in the Foreign eXchange (FX) markets and evaluates corresponding risks.
Better estimation of financial risks possible with maths
Due to the recent financial crisis, the requirements imposed on banks have been made stricter. Banks must model the credit risk of the counterparties now in their portfolios, for instance. A measure for this is the credit value adjustment (CVA): the difference between the value of a portfolio without credit risk and the value if a possible bankruptcy of the counterparty is included. Qian Feng modelled CVAs and designed a new algorithm that can help banks estimate the risks precisely, so they can take appropriate measures if necessary.
The Netherlands’ smallest supercomputer is here
A team of Dutch scientists has built a supercomputer the size of four pizza boxes. The Little Green Machine II has the computing power of 10,000 PCs and will be used by researchers in oceanography, computer science, artificial intelligence, financial modeling and astronomy. CWI researchers Joost Batenburg and Kees Oosterlee, who were part of the development team, will use the machine for computational imaging and machine learning for time series respectively. The computer is based at Leiden University (the Netherlands) and developed with help from IBM.
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.
Members
Associated Members
Publications
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Koren, B. (2022). Denker en doener : in memoriam Piet Hemker (1941-2019). Nieuw Archief voor Wiskunde, 23(2), 113–118.
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van Halder, Y. (2022, January 18). Efficient sampling and solver enhancement for uncertainty quantification.
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Boonstra, B.C, & Oosterlee, C.W. (2021). Valuation of electricity storage contracts using the COS method. Applied Mathematics and Computation, 410. doi:10.1016/j.amc.2021.126416
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Gugole, F, Coffeng, L.E, Edeling, W.N, Sanderse, B, de Vlas, S.J, & Crommelin, D.T. (2021). Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model. PLoS Computational Biology, 17(9). doi:10.1371/journal.pcbi.1009355
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Crommelin, D.T, & Edeling, W.N. (2021). Resampling with neural networks for stochastic parameterization in multiscale systems. Physica - D, Nonlinear Phenomena, 422. doi:10.1016/j.physd.2021.132894
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Vassaux, M, Wan, S, Edeling, W.N, & Coveney, P.V. (2021). Ensembles are required to handle aleatoric and parametric uncertainty in molecular dynamics simulation. Journal of Chemical Theory and Computation, 17(8), 5187–5197. doi:10.1021/acs.jctc.1c00526
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van den Oord, G, Chertova, M, Jansson, F.R, Pelupessy, F.I, Siebesma, A.P, & Crommelin, D.T. (2021). Performance optimization and load-balancing modeling for superparametrization by 3D LES. In Proceedings of the Platform for Advanced Scientific Computing Conference (pp. 1–8). doi:10.1145/3468267.3470611
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Suleimenova, D, Arabnejad, H, Edeling, W.N, Coster, D.P, Luk, O.O, Lakhlili, J, … Groen, D. (2021). Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit. Journal of Computational Science, 53. doi:10.1016/j.jocs.2021.101402
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Mücke, N.T, Bohte, S.M, & Oosterlee, C.W. (2021). Reduced order modeling for parameterized time-dependent PDEs using spatially and memory aware deep learning. Journal of Computational Science, 53. doi:10.1016/j.jocs.2021.101408
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den Haan, T.R.B, Chau, K.W, van der Schans, M, & Oosterlee, C.W. (2021). Rule-based strategies for dynamic life cycle investment. European Actuarial Journal. doi:10.1007/s13385-021-00283-0
Current projects with external funding
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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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Robust numerical modelling for transient multiphase CO2 transport (SHELL)
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Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
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Discretize first, reduce next: a new paradigm to closure for fluid flow simulation (Vidi Sanderse)
Related partners
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Shell, Amsterdam
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MeitY
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Technische Universiteit Eindhoven
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Universiteit Leiden