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.
Vacancies
No vacancies currently.
News
Reality check for financial risk assessment tool concludes CWI-led project
One of the most promising tools for assessing financial risks proves to hold up after a thorough mathematical gauging. CWI researcher Ki Wai Chau developed a numerical analysis of complex algorithms that are developed to support financial risk management. His method provides a reality check for those algorithms, paving the way for future applications.
Smart mobility start-up Skialabs launched by CWI researchers
Traffic flows in cities could be managed much more efficiently thanks to cutting-edge technology pioneered by the new start-up Skialabs. By using huge dataflows collected in cities, the Skialabs platform provides a real-time view on the mobility flows in the city. This allows creating cost-effective and sustainable mobility services that react instantly to the demands of the end-users.
Extreme events better investigated with new math method
Fortunately, incidents like extreme weather, earthquakes or massive power grid blackouts are a rare occurrence. Analysing properly how likely such rare incidents are to happen can be very valuable, but also very challenging. In his PhD thesis, Krzysztof Bisewski developed mathematical methods that greatly speed up simulations for estimating the probability of rare events.
New data framework illuminates uncertainties in offshore wind farm conditions
Wind speeds and wave heights can have a major effect on offshore wind farms. But because they are correlated, their combined significance for wind farm designs couldn’t be factored in until now. CWI researcher Anne Eggels developed methods which take the effect of such correlations or dependencies into account. Today, she will publicly defend her thesis at the University of Amsterdam.
Current events
Probability Seminar Bálint Négyesi (TUD, DIAM)
- 2020-10-28T16:00:00+01:00
- 2020-10-28T17:00:00+01:00
Probability Seminar Bálint Négyesi (TUD, DIAM)
Start: 2020-10-28 16:00:00+01:00 End: 2020-10-28 17:00:00+01:00
Dear All,
Time: Oct 28, 2020 04:00 PM Amsterdam, Berlin, Rome, Stockholm, Vienna
Join Zoom Meeting
https://uva-live.zoom.us/j/82915951912
Meeting ID: 829 1595 1912
Abstract:
Classical numerical methods face the so-called curse of dimensionality and cannot be used to solve high-dimensional problems. In recent years, multiple approaches have been developed to overcome this computational burden, building on deep learning and showing remarkable empirical success well beyond 10 dimensions. However, such Deep BSDE methods struggle with giving accurate approximations for the Z-process throughout the whole time horizon. In the proposed method, we express the Z-process as the Malliavin derivative of the Y-process, using the Malliavin chain rule. An error analysis is carried out proving the consistency of the algorithms and showing first-order convergence under certain assumptions. Numerical experiments are presented to demonstrate the efficiency of the Malliavin formulation compared to other Deep BSDE solvers.
- Email: Bálint Négyesi
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iCal
Members
Associated Members
Publications
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Rens, E.G, Zeegers, M.T, Rabbers, I, Szabó, A, & Merks, R.M.H. (2020). Autocrine inhibition of cell motility can drive epithelial branching morphogenesis in the absence of growth. Philosophical Transactions of the Royal Society B, 375(1807). doi:10.1098/rstb.2019.0386
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Wing, A, Stauffer, L, Becker, T, Reed, K.A, Ahn, M.-S, Arnold, N.P, … Zhao, M. (2020). Clouds and convective self‐aggregation in a multimodel ensemble of radiative‐convective equilibrium simulations. Journal of Advances in Modeling Earth Systems, 12(9). doi:10.1029/2020MS002138
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Nelemans,, B.K.A, Schmitz, M, Tahir, H, Merks, R.M.H, & Smit, T.H. (2020). Somite Division and New Boundary Formation by Mechanical Strain. iScience, 23(4). doi:10.1016/j.isci.2020.100976
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Edeling, W.N, & Crommelin, D.T. (2020). Reducing data-driven dynamical subgrid scale models by physical constraints. Computers & Fluids, 201. doi:10.1016/j.compfluid.2020.104470
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Chau, K.W. (2020, January 16). Numerical finance with backward stochastic differential equations : an exploration of three schemes.
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Kumar, P, Rodrigo, C, Gaspar, F.J, & Oosterlee, C.W. (2019). A parametric acceleration of multilevel Monte Carlo convergence for nonlinear variably saturated flow. Computational Geosciences. doi:10.1007/s10596-019-09922-8
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Dubinkina, S, & Ruchi, S. (2019). Comparison of regularized ensemble Kalman filter and tempered ensemble transform particle filter for an elliptic inverse problem with uncertain boundary conditions. Computational Geosciences. doi:10.1007/s10596-019-09904-w
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Fontanari, A. (2019, December 10). Lorenz-based quantitative risk management.
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Eggels, A.W. (2019, November 6). Uncertainty quantification with dependent input data : including applications to offshore wind farms.
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Bisewski, K.L. (2019, October 15). Rare event simulation and time discretization.
Current projects with external funding
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Valuation Adjustments for Improved Risk Management (ABC-EU-XVA)
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Physics based ICT: The digital twin in pipelines (DP-Trans)
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Dronesurance
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Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
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Verified Exascale Computing for Multiscale Applications (VECMA)
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WIND Turbine Rotor aeroelasticity UncErtainty quantification (WINDTRUE)
Related partners
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DNV GL Netherlands B.V
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Max Planck Institute for Informatics
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Bull Sas
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CBK Sci Con Ltd
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Bayerische Akademie der Wissenschaften
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MeitY
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Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
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Suzlon Blades Technology
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TNO
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Technische Universiteit Eindhoven
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Brunel University London
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University College London
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Universiteit Leiden
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Universiteit van Amsterdam