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
Vici grants for Nikhil Bansal and Roeland Merks
CWI researchers Nikhil Bansal and Roeland Merks have been awarded Vici grants from NWO. With the 1.5 million euro grant, Bansal and Merks can develop their own research lines in the next five years.
Instable blood supply may help healthy cells compete with tumor cells
Researchers of CWI’s Scientific Computing group have found that instabilities in the blood supply in cancerous tissue can, surprisingly, lead to a less favorable environment for tumor cells. Their findings shed light on the potential negative side effects of current treatments that aim to actually normalize the blood supply in cancerous tissues.
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.
Current events
Uncertainty Quantification Seminar Ivan Yaroslavtsev (TU Delft)
 20190221T15:00:00+01:00
 20190221T16:00:00+01:00
Uncertainty Quantification Seminar Ivan Yaroslavtsev (TU Delft)
Start: 20190221 15:00:00+01:00 End: 20190221 16:00:00+01:00
Title: BurkholderDavisGundy inequalities and stochastic integration in UMD Banach spaces
Abstract: In this talk we will present BurkholderDavisGundy inequalities for general UMD Banach spacevalued martingales. Namely, we will show that for any UMD Banach space X, for any Xvalued martingale M with M_0=0, and for any 1 \leq p < infty:
E sup_{0 \leq s \leq t} M_s^p \eqsim_{p, X} E gamma([M]_t)^p, t \geq 0,
where [M]_t is the covariation bilinear form of M defined on X* x X* by
[M]_t(x*, y*) = [<M,x*>,<M, y*>]_t, for x*, y* in X*,
and gamma([M]_t) is the L2norm of a Gaussian measure on X having [M]_t as its covariance bilinear form.
As a consequence we will extend the theory of vectorvalued stochastic integration with respect to a cylindrical Brownian motion by van Neerven, Veraar, and Weis, to the full generality.
PhD defence Anton van der Stoep (Scientific Computing)
 20190326T12:30:00+01:00
 20190326T13:30:00+01:00
PhD defence Anton van der Stoep (Scientific Computing)
Start: 20190326 12:30:00+01:00 End: 20190326 13:30:00+01:00
You are cordially invited to the public defence of Anton van der Stoep on his PhD thesis titled:
Pricing and Calibration with Stochastic Local Volatility Models in a Monte Carlo Setting
Promotor: prof. dr. C.W. Oosterlee (CWI, TU Delft)
Copromotor: dr. ir. L.A. Grzelak (Rabobank, TU Delft)
CI/SC Seminar Cristóbal Bertoglio, Bernoulli Institute
 20190409T11:00:00+02:00
 20190409T12:00:00+02:00
CI/SC Seminar Cristóbal Bertoglio, Bernoulli Institute
Start: 20190409 11:00:00+02:00 End: 20190409 12:00:00+02:00
Inverse problems in hemodynamics from MRI
We will present recent advances and future challenges in the field of databased mathematical modeling of blood flows with data coming from Magnetic Resonance Imaging (MRI). Specifically, we will explore different inverse problems when going from more to less measured data: (a) Pressure maps estimation from 3D+time velocity fields, (b) Parameter estimation from 2D velocity fields (c) extension to parameter estimation from highly undersampled raw MRI data.
Members
 Krzysztof Bisewski
 Anastasia Borovykh
 Laurent van den Bos
 Jurriaan Buist
 Daan Crommelin
 Svetlana Dubinkina
 Wouter Edeling
 Anne Eggels
 Yous van Halder
 Fredrik Jansson
 Barry Koren
 Prashant Kumar
 Bart de Leeuw
 Hemaditya Malla
 Roeland Merks
 Kees Oosterlee
 Sangeetika Ruchi
 Beatriz Salvador Mancho
 Benjamin Sanderse
Associated Members
Publications

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. 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

Fontanari, A, Taleb, N.N, & Cirillo, P. (2018). Gini estimation under infinite variance. Physica A: Statistical Mechanics and its Applications, 502, 256–269. doi:10.1016/j.physa.2018.02.102
Current projects with external funding

Valuation Adjustments for Improved Risk Management ( ABCEUXVA)

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

Geometric Structure and Data Assimilation

Towards cloudresolving climate simulations

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

SE Blades Technology B.V.

Shell, Amsterdam

Bull Sas

CBK Sci Con Ltd

Bayerische Akademie der Wissenschaften

Instytut Chemii Bioorganicznej Polskiej Akademii Nauk

Rijksuniversiteit Groningen

TNO

Technische Universiteit Eindhoven

Technische Universiteit Delft

Brunel University London

University College London

Universiteit van Amsterdam