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, 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, 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.
Vacancies
No vacancies currently.
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

van den Bos, L.M.M, & Sanderse, B. (2021). A geometrical interpretation of the addition of nodes to an interpolatory quadrature rule while preserving positive weights. Journal of Computational and Applied Mathematics, 391. doi:10.1016/j.cam.2021.113430

Salvador Mancho, B, & Oosterlee, C.W. (2021). Total value adjustment for a stochastic volatility model. A comparison with the Black–Scholes model. Applied Mathematics and Computation, 391. doi:10.1016/j.amc.2020.125489

Verheul, N, & Crommelin, D.T. (2021). Stochastic parametrization with VARX processes. Communications in Applied Mathematics and Computational Science, 16(1), 33–57. doi:10.2140/CAMCOS.2021.16.33

Kumar, P, Sanderse, B, Boorsma, K, & Caboni, M. (2020). Global sensitivity analysis of model uncertainty in aeroelastic wind turbine models. In Journal of Physics: Conference Series. doi:10.1088/17426596/1618/4/042034

Wright, D.W, Richardson, R.A, Edeling, W.N, Lakhlili, J, Sinclair, R, Jancauskas, V, … Coveney, P.V. (2020). Building Confidence in Simulation: Applications of EasyVVUQ. Advanced Theory and Simulations, 3(8). doi:10.1002/adts.201900246

Rens, E.G, Zeegers, M.T, Rabbers, I, Szabó, A, & Merks, R.M.H. (2020). Autocrine inhibition of cell motility can drive epithelial branching morphogenesis in the absence of growth. Philosophical Transactions of the Royal Society B, 375(1807). doi:10.1098/rstb.2019.0386

Wing, A, Stauffer, L, Becker, T, Reed, K.A, Ahn, M.S, Arnold, N.P, … Zhao, M. (2020). Clouds and convective self‐aggregation in a multimodel ensemble of radiative‐convective equilibrium simulations. Journal of Advances in Modeling Earth Systems, 12(9). doi:10.1029/2020MS002138

van den Oord, G, Jansson, F.R, Pelupessy, F.I, Chertova, M, Grönqvist, J.H, Siebesma, A.P, & Crommelin, D.T. (2020). A Python interface to the Dutch Atmospheric LargeEddy Simulation. SoftwareX, 12. doi:10.1016/j.softx.2020.100608

Gugole, F, & Franzke, C.L.E. (2020). Spatial covariance modeling for stochastic subgridscale parameterizations using dynamic mode decomposition. Journal of Advances in Modeling Earth Systems, 12. doi:10.1029/2020MS002115

Richardson, R.A, Wright, D.W, Edeling, W.N, Jancauskas, V, Lakhlili, J, & Coveney, P.V. (2020). EasyVVUQ: A library for verification, validation and uncertainty quantification in high performance computing. Journal of Open Research Software, 8(1), 1–8. doi:10.5334/JORS.303
Current projects with external funding

Valuation Adjustments for Improved Risk Management (ABCEUXVA)

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

Discretize first, reduce next: a new paradigm to closure for fluid flow simulation (None)

Unravelling Neural Networks with StructurePreserving Computing (Unravelling Neural Networks)

Verified Exascale Computing for Multiscale Applications (VECMA)
Related partners

Max Planck Institute for Informatics

Bull Sas

CBK Sci Con Ltd

Bayerische Akademie der Wissenschaften

MeitY

Instytut Chemii Bioorganicznej Polskiej Akademii Nauk

Technische Universiteit Eindhoven

Brunel University London

University College London

Universiteit Leiden

Universiteit van Amsterdam