Benjamin Sanderse

Full Name B. Sanderse
+31 20 592 4085
Scientific Staff Member, Group leader
Benjamin Sanderse


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


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.


  • Wind energy and in particular turbulent wind turbine wakes.
  • Multi-phase flow such as CO2 transport in pipelines and reservoirs.
  • Sloshing of liquids in tankers (SLING project).


All publications


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

Professional activities

  • Guest editor: ERCIM News, ‘Digital Twins'
  • Speaker: ACOS symposium (Applied Computational Sciences)
  • Speaker: FYSICA conference, University of Twente
  • Committee member: Platform Wiskunde Nederland - Commissie Innovative
  • Speaker: Data-driven physics simulation (DDPS) webinar of Lawrence Livermore National Laboratory
  • Speaker: Woudschoten conference. Yearly gathering of the Dutch-Flemish scientific computing society. Title: "Structure-preserving learning of embedded discrete closure models ".

Current projects with external funding

  • Robust numerical modelling for transient multiphase CO2 transport (SHELL)
  • Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
  • Discretize first, reduce next: a new paradigm to closure for fluid flow simulation (Vidi Sanderse)