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
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.
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.
Current events
PhD defense Anne Eggels (SC)
- 2019-11-06T10:00:00+01:00
- 2019-11-06T11:30:00+01:00
PhD defense Anne Eggels (SC)
Start: 2019-11-06 10:00:00+01:00 End: 2019-11-06 11:30:00+01:00
You are cordially invited to the public defense of Anne Eggels on her thesis:
Uncertainty quantification with dependent input data
including applications to offshore wind farm
Promotor: Prof.dr. Daan Crommelin
Co-promotor: prof.dr.ir. Barry Koren
PhD defense Ki Wai Chau (SC)
- 2020-01-16T10:00:00+01:00
- 2020-01-16T11:00:00+01:00
PhD defense Ki Wai Chau (SC)
Start: 2020-01-16 10:00:00+01:00 End: 2020-01-16 11:00:00+01:00
You are cordially invited to the public defense of Ki Wai Chau on his PhD thesis titled:
Numerical Finance with Backward Stochastic Differential Equations
Promotor: prof. dr. Cornelis W. Oosterlee (CWI, TU Delft)
- Email: Ki Wai Chua
-
iCal
Members
Associated Members
Publications
-
Liu, S, Borovykh, A.I, Grzelak, L.A, & Oosterlee, C.W. (2019). A neural network-based framework for financial model calibration. Journal of Mathematics in Industry, 9(1). doi:10.1186/s13362-019-0066-7
-
Borovykh, A.I, Oosterlee, C.W, & Bohte, S.M. (2019). Generalization in fully-connected neural networks for time series forecasting. Journal of Computational Science, 36, 1–15. doi:10.1016/j.jocs.2019.07.007
-
Kumar, P. (2019, July 16). Multilevel solvers for stochastic fluid flows.
-
Groen, D, Richardson, R.A, Wright, D.W, Jancauskas, V, Sinclair, R, Karlshoefer, P, … Coveney, P.V. (2019). Introducing VECMAtk - Verification, validation and uncertainty quantification for multiscale and HPC simulations. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 479–492). doi:10.1007/978-3-030-22747-0_36
-
Sanderse, B, & Veldman, A.E.P. (2019). Constraint-consistent Runge–Kutta methods for one-dimensional incompressible multiphase flow.
-
Viebahn, J.P, Crommelin, D.T, & Dijkstra, H.A. (2019). Towards a turbulence closure based on energy modes. Journal of Physical Oceanography, 49(4), 1075–1097. doi:10.1175/JPO-D-18-0117.1
-
Wolff, H.B, Davidson, L.A, & Merks, R.M.H. (2019). Adapting a plant tissue model to animal development: Introducing cell sliding into VirtualLeaf. Bulletin of Mathematical Biology, 1–20. doi:10.1007/s11538-019-00599-9
-
van der Stoep, A.W. (2019, March 26). Pricing and calibration with stochastic local volatility models in a Monte Carlo setting.
-
Bisewski, K.L, Crommelin, D.T, & Mandjes, M.R.H. (2018). Simulation-based assessment of the stationary tail distribution of a stochastic differential equation. In Proceedings of the 2018 Winter Simulation Conference.
-
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
Current projects with external funding
-
Accurate prediction of slugs in multiphase pipe flow simulation for improved oil and gas production
-
Towards cloud-resolving climate simulations
-
Valuation Adjustments for Improved Risk Management (ABC-EU-XVA)
-
Physics based ICT: The digital twin in pipelines (DP-Trans)
-
Excellence in Uncertainty Reduction of Offshore Wind Systems / Loads and Damage (EUROS)
-
Sloshing of Liquefied Natural Gas: subproject Variability (14-10-project2) (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