Description
Leader of the group Scientific Computing: Daan Crommelin.
The world is full of uncertainties. Being able to assess those uncertainties and their impact on predictions is critical for many real-world problems. Our research group works to investigate and develop methods that contribute to a better understanding of hard-to-predict developments in vital areas such as climate, energy, and finance. It’s all about finding efficient mathematical solutions for complex problems and thereby increasing understanding. With the computational methods we develop, it is possible to quantify the range of potential outcomes of systems that are extremely difficult to predict, enabling better forecasting. Our work is targeted in particular at applications in energy systems, finance and climate science. We always aim to make the connection between fundamental research and practical application.
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.
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
Applied Mathematics Techniques for Energy Markets in Transition
- 2017-09-18T09:00:00+02:00
- 2017-09-22T16:00:00+02:00
Applied Mathematics Techniques for Energy Markets in Transition
Start: 2017-09-18 09:00:00+02:00 End: 2017-09-22 16:00:00+02:00
Scientific organizers:
Matthias Ehrhardt (Wuppertal, Germany)
Karel In ’t Hout (Antwerp, Belgium)
Cornelis Oosterlee (Amsterdam, The Netherlands)
Description and Aim
The world is witnessing a tremendous change in its energy supply mix, demand behavior and market dynamics. Pivotal developments include ambitious climate change and environmental policies, the progressive move to sustainable energy, the (at times sudden) abandonment of polluting electricity generation, the growing availability of liquefied natural gas and shale oil and gas. This all has a significant impact on the core business and risk exposures of energy companies, on commodity and energy prices, and also on the many financial energy derivative products traded. Changes in market mechanisms and products demand novel mathematical models, stochastic and deterministic, microscopic and macroscopic models, and changing pricing techniques, defining new research areas within the field of applied mathematics.
Energy markets present unique challenges given very specific, inherent features, like (practical) non-storability of electricity, seasonality trends and dramatic price spikes, complex (often embedded) financial derivative structures, and strong dependence on fundamental factors or political decision making. Furthermore, seasonal patterns exist in demand and across various exogenous and endogenous fundamental price drivers, e.g., fuel prices, emission prices, weather conditions, market coupling mechanisms. Finally, there are often strong regulations or subsidies by governmental authorities, driven by energy and climate control policies. All of these require specially tailored mathematical modelling and methods adapted to energy market applications, e.g., forward backward stochastic differential equations (FBSDEs), Monte Carlo (MC) methods, nonlinear partial (integro) differential equations (PDEs, PIDES), high-dimensionality, sparse grids, recombination techniques, agent-based models and mean field game models.
In this workshop we wish to focus on three relevant, contemporary mathematical themes to cover the different aspects. We envision ample, lively discussions between experts in the fields of applied mathematics, computer science (heuristic algorithms, big data and machine learning), as well as economics (the impact of a major transition). This makes our foreseen workshop highly interactive and a stimulating, challenging experience.
- Web
- Email: Martijn Fritsen
- Phone: 071 527 5542
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iCal
Members of Scientific Computing
- Daan van den Assem
- Debarati Bhaumik
- Krzysztof Bisewski
- Laurent van den Bos
- Ki Chau
- Daan Crommelin
- Svetlana Dubinkina
- Anne Eggels
- Andrea Fontanari
- Francisco Gaspar
- Yous van Halder
- Fredrik Jansson
- Barry Koren
- Prashant Kumar
- Bart de Leeuw
- Roeland Merks
- Sirshendu Misra
- Kees Oosterlee
- Lisanne Rens
- Sangeetika Ruchi
- Benjamin Sanderse
- Anton van der Stoep
- Jan Viebahn
- Mathe Zeegers
Publications
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Sanderse, B, Eskerud Smith, I, & Hendrix, M.H.W. (2017). Analysis of time integration methods for the compressible two-fluid model for pipe flow simulations. International Journal of Multiphase Flow, 95, 155–174. doi:10.1016/j.ijmultiphaseflow.2017.05.002
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van der Stoep, A.W, Grzelak, L.A, & Oosterlee, C.W. (2017). A novel Monte Carlo approach to hybrid local volatility models. Quantitative Finance, 17(9), 1–20. doi:10.1080/14697688.2017.1280613
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Leitao Rodriguez, A, Oosterlee, C.W, Ortiz Gracia, L, & Bohte, S.M. (2017). On the data-driven COS method. doi:10.1016/j.amc.2017.09.002
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Wijnen, T.P.G, Pelupessy, F.I, Pols, O.R, & Portegies Zwart, S. (2017). Changes in orientation and shape of protoplanetary discs moving through an ambient medium. Astronomy and Astrophysics, 604. doi:10.1051/0004-6361/201730793
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Maree, S.C, Ortiz Gracia, L, & Oosterlee, C.W. (2017). Pricing early-exercise and discrete barrier options by Shannon wavelet expansions. Numerische Mathematik, 1–36. doi:10.1007/s00211-016-0858-2
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de Leeuw, B.M, Dubinkina, S, Frank, J.E, Steyer, A, Tu, X, & van Vleck, E. (2017). Projected Shadowing-based Data Assimilation.
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Borovykh, A, Pascucci, A, & Oosterlee, C.W. (2017). Pricing Bermudan options under local Lévy models with default. Journal of Mathematical Analysis and Applications. doi:10.1016/j.jmaa.2017.01.071
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Baars, S, Viebahn, J.P, Mulder, T.E, Kuehn, C, Wubs, F.W, & Dijkstra, H.A. (2017). Continuation of probability density functions using a generalized Lyapunov approach. Journal of Computational Physics, 336, 627–643. doi:10.1016/j.jcp.2017.02.021
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van den Bos, L.M.M, Koren, B, & Dwight, R.P. (2017). Non-intrusive uncertainty quantification using reduced cubature rules. Journal of Computational Physics, 332, 418–445. doi:10.1016/j.jcp.2016.12.011
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Grzelak, L.A, & Oosterlee, C.W. (2017). From arbitrage to arbitrage-free implied volatilities. Journal of Computational Finance, 20(3), 31–49. doi:10.21314/JCF.2016.316
Current projects with external funding
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Accurate prediction of slugs in multiphase pipe flow simulation for improved oil and gas production
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Geometric Structure and Data Assimilation
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Probabilistic Uncertainty Assessments in Energy-Related Problems
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Stochastic models for unresolved scales in geophysical flows
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Towards cloud-resolving climate simulations
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Uncertainty Quantication in Hydraulic Fracturing using Multi-Level Monte Carlo and Multigrid
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EUROSExcellence in Uncertainty Reduction of Offshore Wind Systems
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PORO SOSEfficient numerical methods for deformable porous media. Application to carbon dioxide storage
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RESClimRare Event Simulation for Climate Extremes
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SLINGSloshing of Liquefied Natural Gas: subproject Variability (14-10-project2)
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WAKEUPCALLApplied mathematics for risk measures in finance and insurance, in the wake of the crisis
Related partners
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Vortech
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Rijksuniversiteit Groningen
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Technische Universiteit Eindhoven
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Technische Universiteit Delft