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

van der Stoep, A.W. (2019, March 26). Pricing and calibration with stochastic local volatility models in a Monte Carlo setting.

Bisewski, K, Crommelin, D.T, & Mandjes, M.R.H. (2018). Simulationbased assessment of the stationary tail distribution of a stochastic differential equation. In Proceedings of the 2018 Winter Simulation Conference.

SuárezTaboada, 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/s1336201800472

Jüling, A, Viebahn, J.P, Drijfhout, S.S, & Dijkstra, H.A. (2018). Energetics of the Southern Ocean Mode. Journal of Geophysical Research: Oceans. doi:10.1029/2018JC014191

Viebahn, J.P, Crommelin, D.T, & Dijkstra, H.A. (2018). Towards a turbulence closure based on energy modes. Journal of Physical Oceanography. doi:10.1175/JPOD180117.1

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

Jansson, F, van den Oord, G.J.W.M, Siebesma, A.P, & Crommelin, D.T. (2018). Resolving clouds in a global atmosphere model  a multiscale approach with nested models. In Proceedings  IEEE 14th International Conference on eScience, eScience 2018. doi:10.1109/eScience.2018.00043

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

Eggels, A.W, & Crommelin, D.T. (2018). UQ with dependent inputs: Wind and waves. In Proceedings of ECCM 6 and ECFD 7.

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
Current projects with external funding

Accurate prediction of slugs in multiphase pipe flow simulation for improved oil and gas production

Towards cloudresolving climate simulations

Valuation Adjustments for Improved Risk Management (ABCEUXVA)

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

Excellence in Uncertainty Reduction of Offshore Wind Systems (EUROS)

Rare Event Simulation for Climate Extremes (RESClim)

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

Verified Exascale Computing for Multiscale Applications (VECMA)

WIND Turbine Rotor aeroelasticity UncErtainty quantification (WINDTRUE)
Related partners

DNV GL Netherlands B.V

FOM

Max Planck Institute for Informatics

Shell, Amsterdam

Bull Sas

CBK Sci Con Ltd

Bayerische Akademie der Wissenschaften

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 van Amsterdam