Benjamin Sanderse
- Full Name
- Dr.ir. B. Sanderse
- Function(s)
- Researcher, Group leader
- B.Sanderse@cwi.nl
- Telephone
- +31 20 592 4085
- Room
- L123
- Department(s)
- Scientific Computing
- Homepage
- http://www.thinkingslow.nl/
Biography
Benjamin Sanderse is the group leader of the Scientific Computing group. His work focuses on development of numerical methods for uncertainty quantification, for tackling closure problems, for constructing reduced order models, with the overarching theme of using structure-preserving techniques and applying them to solve complex partial differential equations, for example occurring in fluid flow problems. Prior to his tenure track position, he worked at Shell Technology Centre Amsterdam on research and development of multiphase flow simulators in oil and gas applications. His PhD research was on new numerical methods for simulating incompressible flows occurring in wind energy applications, a combined position at Energy research Centre of the Netherlands (ECN) and CWI. He obtained his PhD degree cum laude (with honours) in 2013 from Eindhoven University of Technology. Before starting his PhD degree, he received his MSc degree in Aerospace Engineering at Delft University of Technology in 2008. For more information, please visit http://www.thinkingslow.nl.Research
My research interest is to develop efficient methods for making predictions under uncertainty of physical systems by developing new methodologies that combine physical modelling approaches and data-driven techniques, in particular to applications involving fluid dynamic problems.
Currently the following topics have my main interest:
- Reduced order models: development of structure-preserving methods that have enhanced stability and accuracy.
- Structure-preserving neural networks and machine learning.
- Closure modeling for multiscale problems, such as in turbulence.
- Uncertainty quantification: surrogate modelling for parametric PDEs, efficient solvers for Bayesian inverse problems, statistical learning for closure models, novel quadrature rules.
- Computational fluid dynamics (CFD): time integration methods for differential-algebraic equations arising from fluid dynamic problems, such as incompressible single-phase and multi-phase Navier-Stokes equations; structure-preserving discretization methods.
Applications:
- Wind energy and in particular turbulent wind turbine wakes (EUROS project).
- Multi-phase flow in reservoirs, wells, and pipelines (with Shell).
- Sloshing of liquids in tankers (SLING project).
Publications
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Gugole, F, Coffeng, L.E, Edeling, W.N, Sanderse, B, de Vlas, S.J, & Crommelin, D.T. (2021). Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model. PLoS Computational Biology, 17(9). doi:10.1371/journal.pcbi.1009355
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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
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Kelbij Star, S.K, Sanderse, B, Stabile, G, Rozza, G, & Degroote, J. (2021). Reduced order models for the incompressible Navier-Stokes equations on collocated grids using a ‘discretize-then-project’ approach. International Journal for Numerical Methods in Fluids, 93(8), 2694–2722. doi:10.1002/fld.4994
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Sanderse, B, Buist, J.F.H, & Henkes, R.A.W.M. (2021). A novel pressure-free two-fluid model for one-dimensional incompressible multiphase flow. Journal of Computational Physics, 426, 109919‐1–109919‐18. doi:10.1016/j.jcp.2020.109919
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van Halder, Y, Sanderse, B, & Koren, B. (2021). PDE/PDF-informed adaptive sampling for efficient nonintrusive surrogate modeling. International Journal for Uncertainty Quantification, 11(6), 83–108. doi:10.1615/Int.J.UncertaintyQuantification.2021034265
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Sanderse, B. (2020). Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods. Journal of Computational Physics, 421. doi:10.1016/j.jcp.2020.109736
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Caboni, M, Carrion, M, Rodriguez, C, Schepers, G, Boorsma, K, & Sanderse, B. (2020). Assessment of sensitivity and accuracy of BEM-based aeroelastic models on wind turbine load predictions. In Journal of Physics: Conference Series. doi:10.1088/1742-6596/1618/4/042015
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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
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van den Bos, L.M.M, Sanderse, B, & Bierbooms, W.A.A.M. (2020). Adaptive sampling-based quadrature rules for efficient Bayesian prediction. Journal of Computational Physics, 417. doi:10.1016/j.jcp.2020.109537
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van den Bos, L.M.M, Bierbooms, W.A.A.M, Alexandre, A, Sanderse, B, & van Bussel, G.J.W. (2020). Fatigue design load calculations of the offshore NREL 5 MW benchmark turbine using quadrature rule techniques. Wind Energy, 23(5), 1181–1195. doi:10.1002/we.2470
Current projects with external funding
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Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
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Discretize first, reduce next: a new paradigm to closure for fluid flow simulation (Vidi Sanderse)
Awards
- Stieltjes prijs 2013 (2013)
- 5th PhD seminar on wind energy in Europe, Durham - Best paper 30 September - 1 October (2009)