Wouter Edeling
- Full Name
- Dr. W.N. Edeling
- Function(s)
- Scientific Staff Member, Chair Works Council
- Wouter.Edeling@cwi.nl
- Telephone
- +31 20 592 4161
- Room
- L124
- Department(s)
- Scientific Computing
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).
Publications
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Groen, D, Arabnejad, H, Suleimenova, D, Edeling, W.N, Raffin, E, Xue, Y, … Coveney, P.V. (2023). FabSim3: An automation toolkit for verified simulations using high performance computing. Computer Physics Communications, 283, 108596.1–108596.18. doi:10.1016/j.cpc.2022.108596
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Bronik, K, Roa, W.M, Vassaux, M, Edeling, W.N, & Coveney, P.V. (2022). Automated variance-based sensitivity analysis of a heterogeneous atomistic-continuum system. In Proceedings of ICCS 2022 (pp. 762–766). doi:10.1007/978-3-031-08760-8_62
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Gugole, F, Coffeng, L.E, Edeling, W.N, Sanderse, B, de Vlas, S.J, & Crommelin, D.T. (2021). Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model. PLoS Computational Biology, 17(9). doi:10.1371/journal.pcbi.1009355
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Crommelin, D.T, & Edeling, W.N. (2021). Resampling with neural networks for stochastic parameterization in multiscale systems. Physica - D, Nonlinear Phenomena, 422. doi:10.1016/j.physd.2021.132894
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Vassaux, M, Wan, S, Edeling, W.N, & Coveney, P.V. (2021). Ensembles are required to handle aleatoric and parametric uncertainty in molecular dynamics simulation. Journal of Chemical Theory and Computation, 17(8), 5187–5197. doi:10.1021/acs.jctc.1c00526
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Suleimenova, D, Arabnejad, H, Edeling, W.N, Coster, D.P, Luk, O.O, Lakhlili, J, … Groen, D. (2021). Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit. Journal of Computational Science, 53. doi:10.1016/j.jocs.2021.101402
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Jansson, F.R, Edeling, W.N, Attema, J, & Crommelin, D.T. (2021). Assessing uncertainties from physical parameters and modelling choices in an atmospheric large eddy simulation model. Philosophical Transactions of the Royal Society A , 379(2197). doi:10.1098/rsta.2020.0073
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Suleimenova, D, Arabnejad, H, Edeling, W.N, & Groen, D. (2021). Sensitivity-driven simulation development: A case study in forced migration. Philosophical Transactions of the Royal Society A , 379(2197). doi:10.1098/rsta.2020.0077
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Ye, D, Veen, L, Nikishova, A, Lakhlili, J, Edeling, W.N, Luk, O.O, … Hoekstra, A.G. (2021). Uncertainty quantification patterns for multiscale models. Philosophical Transactions of the Royal Society A , 379(2197). doi:10.1098/rsta.2020.0072
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Groen, D, Arabnejad, H, Jancauskas, V, Edeling, W.N, Jansson, F.R, Richardson, R.A, … Coveney, P.V. (2021). VECMAtk: a scalable verification, validation and uncertainty quantification toolkit for scientific simulations. Philosophical Transactions of the Royal Society A , 379(2197). doi:10.1098/rsta.2020.0221
Current projects with external funding
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Learning small closure models for large multiscale problems. (None)