Wouter Edeling

Biography

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).