Description
Leader of the group Scientific Computing: Daan Crommelin.
The SC groups develops efficient mathematical methods to simulate and predict real-world 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 data-driven modeling, plays an important role in our research.
News
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.
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.
Current events
SC Seminar Deepesh Toshniwal (Univ. of Texas ICES)
- 2018-10-18T11:00:00+02:00
- 2018-10-18T12:00:00+02:00
SC Seminar Deepesh Toshniwal (Univ. of Texas ICES)
Start: 2018-10-18 11:00:00+02:00 End: 2018-10-18 12:00:00+02:00
Date: Thursday 18 October
Time: 11h00
Room: L120
Speaker: Deepesh Toshniwal (ICES, The University of Texas at Austin, working with prof. T. Hughes)
Title: High-order structure-preserving splines for incompressible flow simulation
Abstract: In this talk, we will discuss the construction of high-order spline basis functions that span gradient, curl and divergence conforming spaces with applications to electromagnetism and fluid flows in mind. Stability of the resulting numerical schemes is complemented by pointwise satisfaction of conservation laws — such as mass conservation in incompressible flow simulations — on arbitrarily curved geometries even at the coarsest levels of discretization. Examples demonstrating the robustness and applicability of the spline spaces will be presented. In particular, they will be used to simulate Stokes flow on curved surfaces that contain parameterization singularities.
- Web
- Email: Benjamin Sanderse
-
iCal
Members
Associated Members
Publications
-
Suárez-Taboada, 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/s13362-018-0047-2
-
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
-
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
-
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
-
Grzelak, L.A, Witteveen, J.A.S, Suárez-Taboada, M, & Oosterlee, C.W. (2018). The stochastic collocation Monte Carlo sampler: highly efficient sampling from ‘expensive’ distributions. Quantitative Finance. doi:10.1080/14697688.2018.1459807
-
Bhaumik, D. (2018, May 30). Computational techniques for assessing power grids with wind energy and storage.
-
Bhaumik, D, Crommelin, D.T, Kapodistria, S, & Zwart, A.P. (2018). Hidden Markov Models for wind farm power output. IEEE Transactions on Sustainable Energy. doi:10.1109/TSTE.2018.2834475
-
Colldeforns-Papiol, G, Ortiz Gracia, L, & Oosterlee, C.W. (2018). Quantifying credit portfolio losses under multi-factor models. International Journal of Computer Mathematics. doi:10.1080/00207160.2018.1447666
-
Maree, S.C, Ortiz Gracia, L, & Oosterlee, C.W. (2018). Fourier and wavelet option pricing methods. In M.A.H Dempster, J Kanniainen, J Keane, & E Vynckier (Eds.), High-Performance Computing in Finance: Problems, Methods, and Solutions (pp. 249–272). doi:10.1201/9781315372006
Current projects with external funding
-
Accurate prediction of slugs in multiphase pipe flow simulation for improved oil and gas production
-
Geometric Structure and Data Assimilation
-
Probabilistic Uncertainty Assessments in Energy-Related Problems
-
Towards cloud-resolving climate simulations
-
Uncertainty Quantication in Hydraulic Fracturing using Multi-Level Monte Carlo and Multigrid
-
Excellence in Uncertainty Reduction of Offshore Wind Systems (EUROS)
-
Efficient numerical methods for deformable porous media. Application to carbon dioxide storage (PORO SOS)
-
Rare Event Simulation for Climate Extremes (RESClim)
-
Sloshing of Liquefied Natural Gas: subproject Variability (14-10-project2) (SLING)
-
Verified Exascale Computing for Multiscale Applications (VECMA)
-
Applied mathematics for risk measures in finance and insurance, in the wake of the crisis (WAKEUPCALL)
Related partners
-
FOM
-
Max Planck Institute for Informatics
-
Shell, Amsterdam
-
Vortech
-
Bull Sas
-
CBK Sci Con Ltd
-
Bayerische Akademie der Wissenschaften
-
Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
-
Rijksuniversiteit Groningen
-
Technische Universiteit Eindhoven
-
Technische Universiteit Delft
-
Brunel University London
-
University College London
-
Universiteit van Amsterdam