Short bio

Benjamin Sanderse is head of the Scientific Computing group of Centrum Wiskunde & Informatica (CWI), the Netherlands national research institute for mathematics and computer science in Amsterdam.

Trained as aerospace engineer and with a doctorate in numerical mathematics, I’m passionate about solving complex problems at the interface between top-level scientific research and challenging real-life applications.

I have extensive experience in (computational) fluid dynamics, differential equations, and uncertainty quantification from working in academia, at research institutes and in industry. My current interest lies in the smart integration of data with physical models, through development of techniques such as reduced order models, Bayesian calibration, (neural) closure models, and scientific machine learning algorithms.


Benjamin Sanderse


  • present2023

    Associate professor

    Eindhoven University of Technology

  • present2021
  • present2020

    Senior researcher CWI Amsterdam


  • 20202016

    Tenure track researcher CWI Amsterdam

    Amsterdam, the Netherlands

  • 20182013

    Flow assurance researcher Shell Global Solutions

    Amsterdam, the Netherlands

  • 20102010

    Visiting researcher

    NREL, Colorado, USA

  • 20132008

    PhD candidate: computational fluid dynamics for wind-turbine wake simulation

    ECN Petten; CWI Amsterdam; Eindhoven University of Technology

  • 20082002

    Master of Science, Aerospace Engineering

    Delft University of Technology

The Scientific Computing group at CWI

Discretize first, filter next: model-data consistent closure models for large-eddy simulation

We propose a new neural network based large eddy simulation framework for the incompressible Navier-Stokes equations based on the paradigm […]

Energy-conserving neural networks for turbulence closure modeling

In turbulence modeling, and more particularly in the Large-Eddy Simulation (LES) framework, we are concerned with finding closure models that […]

Stable reduced-order models for fluid flows

Reduced-order models are used widely to make fluid flow simulation computationally tractable for example for the purpose of real-time control [...]

Quantifying uncertainty in fatigue loads on offshore wind turbines

When designing wind turbines, it is necessary to give proof that the turbine will be able to withstand the environmental […]