Benjamin Sanderse

Full Name B. Sanderse
+31 20 592 4085
Scientific Computing


Benjamin Sanderse works as a tenured researcher in the Scientific Computing group, focusing on numerical methods for uncertainty quantification and for solving partial differential equations 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


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 two topics have my main interest:

  • Uncertainty quantification: surrogate modelling for parametric PDEs, efficient solvers for Bayesian inverse problems, statistical learning for closure models, novel quadrature rules (with Laurent van den Bos, Yous van Halder).
  • 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.


  • Wind energy and in particular turbulent wind turbine wakes (EUROS project).
  • Multi-phase flow in reservoirs, wells, and pipelines.
  • Sloshing of liquids in tankers (SLING project).


Current projects with external funding

  • Discretize first, reduce next: a new paradigm to closure for fluid flow simulation (None)
  • Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
  • WIND Turbine Rotor aeroelasticity UncErtainty quantification (WINDTRUE)


  • Stieltjes prijs 2013 (2013)
  • 5th PhD seminar on wind energy in Europe, Durham - Best paper 30 September - 1 October (2009)