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
Five Veni grants for CWI researchers
The Netherlands Organisation for Scientific Research (NWO) has awarded Veni grants to Jop Briët, Jiyin He, Wouter Koolen, Marc Stevens and Xiaodong Zhuge of CWI.
Daan Crommelin professor of Numerical Analysis and Dynamical Systems
Daan Crommelin has been appointed as professor by special appointment of Numerical Analysis and Dynamical Systems at the University of Amsterdam's (UvA) Faculty of Science. The chair was established on behalf of the Stichting voor Hoger Onderwijs in de Toegepaste Wiskunde.
NAW publication on Mathematics and Planet Earth
Daan Crommelin (CWI) and Henk Schuttelaars (TU Delft) were guest editors of a special issue of Nieuw Archief voor Wiskunde (NAW) on Mathematics and Planet Earth that was recently published. Daan Crommelin also wrote a blog post for the Daily Blog of the 'Mathematics of Planet Earth 2013' (MPE) website on this special publication. A summary follows below.
Quality guarantee for tomographic images developed
CWI researcher Wagner Fortes has developed new methods to determine the quality of tomographic images. Tomography is an imaging technique used in for instance medical CT scanners and electron microscopes that uses penetrating beams to record a series of two-dimensional images from a range of angles.
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