Description
Leader of the group Scientific Computing: Daan Crommelin.
The SC groups 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, 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 datadriven modeling, plays an important role in our research.
News
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.
Dealing with uncertainties in simulations
Understanding uncertainties is crucial when designing computer simulations. Incorporating such uncertainties in simulations and mapping the bandwith of possbile values is the central topic of the inaugural lecture of Daan Crommelin at the University of Amsterdam on Thursday 21 April 2016.
Members
Associated Members
Publications

Kumar, P, Schmelzer, M. (Martin), & Dwight, R.P. (2020). Stochastic turbulence modeling in RANS simulations via multilevel Monte Carlo. Computers & Fluids, 201. doi:10.1016/j.compfluid.2019.104420

Chau, K.W, Tang, J, & Oosterlee, C.W. (2020). An SGBMXVA demonstrator: A scalable Python tool for pricing XVA. Journal of Mathematics in Industry, 10(7). doi:10.1186/s13362020000735

van den Bos, L.M.M. (2020, February 4). Quadrature methods for wind turbine load calculations.

Eggels, A.W. (Anne W.), & Crommelin, D.T. (2020). Uncertainty quantification with dependent inputs: Wind and waves. In Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018 (pp. 4099–4110).

Razaaly, N, Crommelin, D.T, & Congedo, P.M. (2019). Efficient estimation of extreme quantiles using adaptive kriging and importance sampling. International Journal for Numerical Methods in Engineering. doi:10.1002/nme.6300

Oosterlee, C.W, & Grzelak, L.A. (2019). Mathematical Modeling and Computation in Finance. World Scientific.

van Halder, Y, Sanderse, B, & Koren, B. (2019). An adaptive minimum spanning tree multielement method for uncertainty quantification of smooth and discontinuous responses. SIAM Journal on Scientific Computing, 41(6), A3624–A3648. doi:10.1137/18M1219643

Ruchi, S, Dubinkina, S, & Iglesias, M.A. (2019). Transformbased particle filtering for elliptic Bayesian inverse problems. Inverse Problems, 35(11). doi:10.1088/13616420/ab30f3

Liu, S, Borovykh, A.I, Grzelak, L.A, & Oosterlee, C.W. (2019). A neural networkbased framework for financial model calibration. Journal of Mathematics in Industry, 9(1). doi:10.1186/s1336201900667

Borovykh, A.I, Oosterlee, C.W, & Bohte, S.M. (2019). Generalization in fullyconnected neural networks for time series forecasting. Journal of Computational Science, 36(101020), 1–15. doi:10.1016/j.jocs.2019.07.007
Current projects with external funding

Towards cloudresolving climate simulations

Valuation Adjustments for Improved Risk Management (ABCEUXVA)

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

Dronesurance

Sloshing of Liquefied Natural Gas: subproject Variability (1410project2) (SLING)

Unravelling Neural Networks with StructurePreserving Computing (Unravelling Neural Networks)

Verified Exascale Computing for Multiscale Applications (VECMA)

WIND Turbine Rotor aeroelasticity UncErtainty quantification (WINDTRUE)
Related partners

DNV GL Netherlands B.V

Max Planck Institute for Informatics

Bull Sas

CBK Sci Con Ltd

Bayerische Akademie der Wissenschaften

Maritiem Research Instituut

MeitY

Instytut Chemii Bioorganicznej Polskiej Akademii Nauk

Rijksuniversiteit Groningen

Suzlon Blades Technology

TNO

Technische Universiteit Eindhoven

Technische Universiteit Delft

Brunel University London

University College London

Universiteit Leiden

Universiteit van Amsterdam