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, and finance. 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
Tackling messy blood vessels to fight cancer
With a Vici grant of 1.5 million euro, Roeland Merks (CWI and UL) looks for ways to fix messy and leaky blood vessels in tumours. To do so, he combines mathematical simulations and lab experiments in a unique way.
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.
Members
Associated Members
Publications
-
van den Bos, L.M.M, & Sanderse, B. (2021). A geometrical interpretation of the addition of nodes to an interpolatory quadrature rule while preserving positive weights. Journal of Computational and Applied Mathematics, 391. doi:10.1016/j.cam.2021.113430
-
Salvador Mancho, B, & Oosterlee, C.W. (2021). Total value adjustment for a stochastic volatility model. A comparison with the Black–Scholes model. Applied Mathematics and Computation, 391. doi:10.1016/j.amc.2020.125489
-
Verheul, N, & Crommelin, D.T. (2021). Stochastic parametrization with VARX processes. Communications in Applied Mathematics and Computational Science, 16(1), 33–57. doi:10.2140/CAMCOS.2021.16.33
-
Kumar, P, Sanderse, B, Boorsma, K, & Caboni, M. (2020). Global sensitivity analysis of model uncertainty in aeroelastic wind turbine models. In Journal of Physics: Conference Series. doi:10.1088/1742-6596/1618/4/042034
-
Wright, D.W, Richardson, R.A, Edeling, W.N, Lakhlili, J, Sinclair, R, Jancauskas, V, … Coveney, P.V. (2020). Building Confidence in Simulation: Applications of EasyVVUQ. Advanced Theory and Simulations, 3(8). doi:10.1002/adts.201900246
-
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
-
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
-
van den Oord, G, Jansson, F.R, Pelupessy, F.I, Chertova, M, Grönqvist, J.H, Siebesma, A.P, & Crommelin, D.T. (2020). A Python interface to the Dutch Atmospheric Large-Eddy Simulation. SoftwareX, 12. doi:10.1016/j.softx.2020.100608
-
Gugole, F, & Franzke, C.L.E. (2020). Spatial covariance modeling for stochastic subgrid-scale parameterizations using dynamic mode decomposition. Journal of Advances in Modeling Earth Systems, 12. doi:10.1029/2020MS002115
-
Richardson, R.A, Wright, D.W, Edeling, W.N, Jancauskas, V, Lakhlili, J, & Coveney, P.V. (2020). EasyVVUQ: A library for verification, validation and uncertainty quantification in high performance computing. Journal of Open Research Software, 8(1), 1–8. doi:10.5334/JORS.303
Current projects with external funding
-
Valuation Adjustments for Improved Risk Management (ABC-EU-XVA)
-
Physics based ICT: The digital twin in pipelines (DP-Trans)
-
Discretize first, reduce next: a new paradigm to closure for fluid flow simulation (None)
-
Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
-
Verified Exascale Computing for Multiscale Applications (VECMA)
Related partners
-
Max Planck Institute for Informatics
-
Bull Sas
-
CBK Sci Con Ltd
-
Bayerische Akademie der Wissenschaften
-
MeitY
-
Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
-
Technische Universiteit Eindhoven
-
Brunel University London
-
University College London
-
Universiteit Leiden
-
Universiteit van Amsterdam