Wouter Edeling

Full Name
Dr. W.N. Edeling
+31 20 592 4161
Scientific Staff Member, Chair Works Council
Wouter Edeling


I am a tenure tracker in the Scientific Computing group at CWI. My background is in aerospace engineering (TU Delft, with honours), and I obtained a joint-PhD from Delft University of Technology and Arts et Métiers ParisTech in 2015 on the topic of uncertainty quantification for Reynolds Averaged Navier-Stokes (RANS) turbulence closures. I'm a recipient of the Center for Turbulence Research Postdoctoral fellowship at Stanford University and worked on model error representation in turbulence models, the use of advanced Bayesian data analysis, and reduced-order modelling for multiscale simulations. My research interest lies at the intersection of uncertainty quantification, multiscale modelling, and machine learning. Some of my current research involves: 1) Creating subgrid-scale models based on a new reduced-order modelling technique. 2) Using neural networks and active subspace ideas for high-dimensional uncertainty quantification. 3) Investigating the stability of coupled machine learning - PDE systems. I am also involved in open-source software development for uncertainty quantification: 1) EasyVVUQ: a Python based library for forward uncertainty quantification and sensitivity analysis: https://github.com/UCL-CCS/EasyVVUQ 2) EasySurrogate: a Python-based library for various surrogate modelling techniques: https://github.com/wedeling/EasySurrogate Highlighted papers: High-dimensional uncertainty quantification for COVID19 modelling: Edeling, Wouter, et al. "The impact of uncertainty on predictions of the CovidSim epidemiological code." Nature Computational Science 1.2 (2021): 128-135. Reducing the unknowns in subgrid-scale models: Edeling, Wouter, and Daan Crommelin. "Reducing data-driven dynamical subgrid scale models by physical constraints." Computers & Fluids 201 (2020): 104470. High-dimensional uncertainty quantification using neural networks: Edeling, Wouter, On the deep active subspace method, submitted (2021).


All publications


  • Stanford university postdoctoral fellowship at the Center for Turbulence Research, 2015-2017. (2015)

Professional activities

  • Speaker: Invited lecture on Uncertainty Quantification at the von Karman institute 2018.
  • Speaker: SIAM UQ18 , Los Angeles.
  • Showcased: Nature news: Adam, D., Simulating the pandemic: What COVID forecasters can learn from climate models, https://www.nature.com/articles/d41586-020-03208-1
  • Showcased: UK science museum: Highfield, R. CORONAVIRUS: VIRTUAL PANDEMICS , https://www.sciencemuseumgroup.org.uk/blog/coronavirus-virtual-pandemics/
  • Organizer: International conference for computational science (ICCS) thematic track organizer in 2020,2021 and 2022.
  • Showcased: Nature news & views : Leung, K., Wu, J.T., Quantifying the uncertainty of CovidSim https://www.nature.com/articles/s43588-021-00031-0
  • Showcased: Folia: Hoebe, H., UvA’ers: Brits coronarekenmodel gebrekkig, aantal doden kan vier keer zo hoog zijn https://www.folia.nl/wetenschap/144554/uvaers-brits-coronarekenmodel-gebrekkig-aantal-doden-kan-vier-keer-zo-hoog-zijn
  • Organizer: Co-organizer "The Role of Uncertainty in Mathematical Modeling of Pandemics” at the Isaac Newton Institute in Cambridge
  • Organizer: Co-organizer “Multiscale Modelling, Uncertainty Quantification and the Reliability of Computer Simulations” online conference, 2022
  • Speaker: 3 talks at Isaac Newton Institute (Cambridge U.): i) The Impact of Uncertainty on the CovidSim Pandemic Code, ii) Tutorial 1 EasyVVUQ, iii) Very Large Parameter Problems.
  • Speaker: Invited talk SCS spring meeting: High-dimensional parametric uncertainty quantification, Leuven, 2022.
  • Speaker: ECCOMAS : Learning reduced subgrid-scale models, Oslo, 2022.
  • Speaker: SIAM UQ 22, Reduced Data-Driven Parameterizations for Turbulent Flow, Atlanta.
  • Speaker: Invited talk CASA Day (Centre for Analysis, Scientific Computing and Applications, TU Eindhoven), April 2023.

Current projects with external funding

  • Learning small closure models for large multiscale problems. (None)