Description
Leader of the group Scientific Computing: Benjamin Sanderse.
The Scientific Computing group at CWI develops efficient mathematical methods to simulate and predict realworld 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, and finance. 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, reduced order modeling, data assimilation, and stochastic multiscale modeling. The availability of data to inform and improve simulations and predictions, for example through learning and datadriven modeling, plays an important role in our research.
The SC group organizes the seminar on Machine Learning and Uncertainty Quantification in Scientific Computing.
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

Groen, D, Arabnejad, H, Suleimenova, D, Edeling, W.N, Raffin, E, Xue, Y, … Coveney, P.V. (2023). FabSim3: An automation toolkit for verified simulations using high performance computing. Computer Physics Communications, 283, 108596.1–108596.18. doi:10.1016/j.cpc.2022.108596

Versluis, D.M, Schoemaker, R, Looijesteijn, E, Muysken, D, Jeurink, P.V, Paques, M, … Merks, R.M.H. (2022). A multiscale spatiotemporal model including a switch from aerobic to anaerobic metabolism reproduces succession in the early infant gut microbiota. mSystems, 7(5), e00446‐22.1–e00446‐22.24. doi:10.1128/msystems.0044622

Liu, S, Leitao Rodriguez, Á, Borovykh, A.I, & Oosterlee, C.W. (2022). On a neural network to extract implied information from American options. Applied Mathematical Finance, 28(5), 449–475. doi:10.1080/1350486X.2022.2097099

Koren, B. (2022). Denker en doener : in memoriam Piet Hemker (19412019). Nieuw Archief voor Wiskunde, 23(2), 113–118.

van Halder, Y. (2022, January 18). Efficient sampling and solver enhancement for uncertainty quantification.

de Leeuw, B.M. (2021, December 22). On shadowing methods for data assimilation.

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

Gugole, F, Coffeng, L.E, Edeling, W.N, Sanderse, B, de Vlas, S.J, & Crommelin, D.T. (2021). Uncertainty quantification and sensitivity analysis of COVID19 exit strategies in an individualbased transmission model. PLoS Computational Biology, 17(9). doi:10.1371/journal.pcbi.1009355

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

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
Current projects with external funding

Physics based ICT: The digital twin in pipelines (DPTrans)

Learning small closure models for large multiscale problems. (None)

Robust numerical modelling for transient multiphase CO2 transport (SHELL)

Unravelling Neural Networks with StructurePreserving Computing (Unravelling Neural Networks)

Discretize first, reduce next: a new paradigm to closure for fluid flow simulation (Vidi Sanderse)
Related partners

Shell, Amsterdam

MeitY

Technische Universiteit Eindhoven

Universiteit Leiden