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
Members
Associated Members
Publications
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Kumar, P, Schmelzer, M. (Martin), & Dwight, R.P. (2020). Stochastic turbulence modeling in RANS simulations via multilevel Monte Carlo. Computers & Fluids, 201. doi:10.1016/j.compfluid.2019.104420
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Chau, K.W, Tang, J, & Oosterlee, C.W. (2020). An SGBM-XVA demonstrator: A scalable Python tool for pricing XVA. Journal of Mathematics in Industry, 10(7). doi:10.1186/s13362-020-00073-5
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van den Bos, L.M.M. (2020, February 4). Quadrature methods for wind turbine load calculations.
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Eggels, A.W. (Anne W.), & Crommelin, D.T. (2020). Uncertainty quantification with dependent inputs: Wind and waves. In Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018 (pp. 4099–4110).
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Razaaly, N, Crommelin, D.T, & Congedo, P.M. (2019). Efficient estimation of extreme quantiles using adaptive kriging and importance sampling. International Journal for Numerical Methods in Engineering. doi:10.1002/nme.6300
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Oosterlee, C.W, & Grzelak, L.A. (2019). Mathematical Modeling and Computation in Finance. World Scientific.
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van Halder, Y, Sanderse, B, & Koren, B. (2019). An adaptive minimum spanning tree multielement method for uncertainty quantification of smooth and discontinuous responses. SIAM Journal on Scientific Computing, 41(6), A3624–A3648. doi:10.1137/18M1219643
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Ruchi, S, Dubinkina, S, & Iglesias, M.A. (2019). Transform-based particle filtering for elliptic Bayesian inverse problems. Inverse Problems, 35(11). doi:10.1088/1361-6420/ab30f3
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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
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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(101020), 1–15. doi:10.1016/j.jocs.2019.07.007
Current projects with external funding
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Towards cloud-resolving climate simulations
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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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Sloshing of Liquefied Natural Gas: subproject Variability (14-10-project2) (SLING)
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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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Maritiem Research Instituut
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MeitY
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Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
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Rijksuniversiteit Groningen
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Suzlon Blades Technology
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TNO
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Technische Universiteit Eindhoven
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Technische Universiteit Delft
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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